Biology subjects

Han, J.

Publications and source records attributed to Han, J..

At least 199 records · Page 11Linked to original sources

bioRxiv2023

Scalable Mesenchymal Stem Cells Enrichment from Bone Marrow Aspirate using Deterministic Lateral Displacement (DLD) Microfluidics Sorting

The growing interest in regenerative medicine has opened new avenues for novel cell therapies using stem cells. Bone Marrow Aspirate (BMA) is an important source of stromal mesenchymal stem cells (MSCs). Conventional MSC harvesting from BMA relies on archaic centrifugation methods, often leading to poor yield due to osmotic stress, high centrifugation force, convoluted workflow, and long experimental time ([~] 2 - 3 hours). To address these issues, we have developed a scalable microfluidic technology based on Deterministic Lateral Displacement (DLD) for MSC isolation. This passive, label-free cell sorting method capitalizes on the morphological differences between MSCs and blood cells (leukocytes and RBCs) for effective separation using an inverted L-shaped pillar array. To improve throughput, we developed a novel portable multiplexed DLD system that can process 2.5 mL of raw BMA in 20 {+/-} 5 minutes, achieving a 2-fold increase in MSC recovery compared to centrifugation methods. Taken together, we envision the developed DLD platform will enable fast and efficient isolation of MSCs from BMA for effective downstream cell therapy in clinical settings.

bioengineering↗

bioRxiv2023

Single-cell mutation rate of turnip crinkle virus (-)-strand replication intermediates

Viruses with single-stranded, positive-sense (+) RNA genomes incur high numbers of errors during replication, thereby creating diversified genome populations from which new, better adapted viral variants can emerge. However, a definitive error rate is known for a relatively few (+) RNA plant viruses, due to challenges to account for perturbations caused by natural selection and/or experimental set-ups. To address these challenges, we developed a new approach that exclusively profiled errors in the (-)-strand replication intermediates of turnip crinkle virus (TCV), in singly infected cells. A series of controls and safeguards were devised to ensure errors inherent to the experimental process were accounted for. This approach permitted the estimation of a TCV error rate of 8.47 X 10-5 substitution per nucleotide site per cell infection. Importantly, the characteristic error distribution pattern among the 50 copies of 2,363-base-pair cDNA fragments predicted that nearly all TCV (-) strands were products of one replication cycle per cell. Furthermore, some of the errors probably elevated error frequencies by lowering the fidelity of TCV RNA-dependent RNA polymerase, and/or permitting occasional re-replication of progeny genomes. In summary, by profiling errors in TCV (-)-strand intermediates incurred during replication in single cells, this study provided strong support for a stamping machine mode of replication employed by a (+) RNA virus. Author SummaryMost (+) RNA viruses introduce replication errors at relatively high frequencies. As a result, it is of vital importance for these viruses to purge lethal errors in a timely manner. TCV, a plant-infecting small (+) RNA virus, was proposed to encode a Bottleneck, Isolate, Amplify, Select (BIAS) mechanism that compel swift clearance of lethal errors by bottlenecking the number of replicating genome copies to one per cell. A crucial prediction of this BIAS model is that such bottlenecking also acts on progeny genome copies, preventing them from repeating replication in the cells of their own genesis. The current study tested this prediction by developing a carefully controlled, readily reproducible approach to profile errors and error distributions in (-)-stranded replication intermediates of TCV. We found that most of replication-generated (-) strands descended from the primary (+) strands through a single replication cycle. This finding adds fresh support to the BIAS model.

evolutionary biology↗

bioRxiv2023

Identification of allele-specific KIV-2 repeats and impact on Lp(a) measurements for cardiovascular disease risk

The abundance of Lp(a) protein holds significant implications for the risk of cardiovascular disease (CVD), which is directly impacted by the copy number (CN) of KIV-2, a 5.5 kbp sub-region. KIV-2 is highly polymorphic in the population and accurate analysis is challenging. In this study, we present the DRAGEN KIV-2 CN caller, which utilizes short reads. Data across 166 WGS show that the caller has high accuracy, compared to optical mapping and can further phase [~]50% of the samples. We compared KIV-2 CN numbers to 24 previously postulated KIV-2 relevant SNVs, revealing that many are ineffective predictors of KIV-2 copy number. Population studies, including USA-based cohorts, showed distinct KIV-2 CN, distributions for European-, African-, and Hispanic-American populations and further underscored the limitations of SNV predictors. We demonstrate that the CN estimates correlate significantly with the available Lp(a) protein levels and that phasing is highly important.

genomics↗

bioRxiv2023

A Machine Learning-optimized system for on demand, pulsatile, photo- and chemo-therapeutic treatment using near-infrared responsive MoS2-based microparticles in a breast cancer model

Cancer therapy research is of high interest because of the persistence and mortality of the disease and the side effects of traditional therapeutic methods, while often multimodal treatments are necessary based on the patients needs. The development of less invasive modalities for recurring treatment cycles is thus of critical significance. Herein, a light-activatable microparticle system was developed for localized, pulsatile delivery of anticancer drugs with simultaneous thermal ablation, by applying controlled ON-OFF thermal cycles using near-infrared laser irradiation. The system is composed of poly(caprolactone) microparticles of 200 m size with incorporated molybdenum disulfide (MoS2) nanosheets as the photothermal agent and hydrophilic doxorubicin or hydrophobic violacein, as model drugs. Upon irradiation the nanosheets heat up to [≥]50 {degrees}C leading to polymer matrix melting and release of the drug. MoS2 nanosheets exhibit high photothermal conversion efficiency and allow for application of low power laser irradiation for the system activation. A Machine Learning algorithm was applied to acquire optimal laser operation conditions; 0.4 W/cm2 laser power at 808 nm, 3-cycle irradiation, for 3 cumulative minutes. In a mouse subcutaneous model of 4T1 triple-negative breast cancer, 25 microparticles were intratumorally administered and after 3-cycle laser treatment the system conferred synergistic phototherapeutic and chemotherapeutic effect. Our on-demand, pulsatile synergistic treatment resulted in increased median survival up to 40 days post start of treatment compared to untreated mice, with complete eradication of the tumors at the primary site. Such a system could have potential for patients in need of recurring cycles of treatment on subcutaneous tumors. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=56 SRC="FIGDIR/small/536750v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@14ab0f3org.highwire.dtl.DTLVardef@a72649org.highwire.dtl.DTLVardef@1c28608org.highwire.dtl.DTLVardef@1ed9c06_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Somatic Calcium Signals from Layer II/III Motor Cortex for Continuous Neural Decoding

The latest research shows that calcium signals can provide a new signal source for brain-machine interfaces (BMI). However, it remains a question whether the calcium signals from layer 2/3 motor cortex can be used for continuous neural decoding. And how they are involved in movement coding is also worth investigating. Here we collect the somatic signals in M1 layer 2/3 while mice performed a lever-press task under the one-photon imaging. We first present the potential of somatic calcium signals from layer 2/3 in continuous neural decoding through an improved recurrent neural network. Layer 2/3 neurons exhibit three types of calcium dynamics with distinct spatiotemporal coding patterns involved in the movement. Pre-pressing and pressing neurons enable sparse coding of movement through complementary spatiotemporal information. While post-pressing neurons predict the lever movement most accurately through the calcium dynamics with higher fidelity. These results demonstrate the capability of calcium signals from layer 2/3 neurons as a motor BMI driver and underscore their diversity in motor coding, opening a new avenue for studying the motor cortex and designing optical BMIs.

neuroscience↗

bioRxiv2023

A network of CD163+ macrophages monitors enhanced permeability at the blood-dorsal root ganglion barrier

In dorsal root ganglia (DRG), macrophages reside in close proximity to sensory neurons, and their functions have largely been explored in the context of pain, nerve injury and repair. In this study, however, we discovered that the majority of macrophages in DRGs are in direct contact with the vasculature where they constantly monitor the circulation, efficiently phagocytosing proteins and macromolecules from the blood. Characterization of the DRG endothelium revealed a specialized vascular network spanning the arteriovenous axis, which gradually transformed from a barrier type endothelium in arteries to a highly permeable endothelium in veins. Macrophage phagocytosis spatially aligned with peak endothelial permeability and we identified caveolar transcytosis as a mechanism regulating endothelial permeability. Profiling of the DRG immune landscape revealed two subsets of perivascular macrophages with distinct transcriptome, turnover and function. CD163 expressing macrophages self-maintained locally, specifically participated in vasculature monitoring, displayed distinct responses during peripheral inflammation and were conserved in mouse and Man. Our work provides a molecular explanation for the permeability of the blood-DRG barrier and identifies an unappreciated role of macrophages as integral components of the DRG-neurovascular unit.

immunology↗

bioRxiv2023

Somatic small-variant calling methods in Illumina DRAGEN™ Secondary Analysis

We present the DRAGEN somatic pipeline for calling small somatic variants from tumor samples, with or without paired normal samples. The DRAGEN somatic variant caller offers 1) a flexible architecture that can be used on a wide array of somatic use cases; 2) built-in noise models enabling robustness against various sources of noise artifacts (mapping, genome context, or sample specific); 3) performance of joint analysis of tumor and normal samples in the case of a tumor-normal workflow yielding improved accuracy; 4) benefits from FPGA acceleration for efficient run time. We demonstrate the speed and accuracy of the DRAGEN tumor-normal pipeline across a range of whole genome sequencing (WGS) datasets and compare against third party tools such as Mutect2/GATK4 [1] and Strelka2 [2]. DRAGEN secondary analysis outperforms all other tools with its ability to complete a 110x/40x T/N whole-genome analysis in less than two hours. It offers exceptional accuracy, with higher sensitivity and precision than third party tools. We also show that the DRAGEN T/N workflow supports analysis of liquid and late-stage solid tumors by tolerating tumor-in-normal (TiN) contamination.

bioinformatics↗

bioRxiv2023

High throughput detection of variation in single-cell whole transcriptome through streamlined scFAST-seq

High-throughput single-cell full-length RNA sequencing is a powerful tool to explore the entire transcriptome, including non-polyadenylated transcripts. We developed a single cell Full-length RNA Sequence Transcriptome sequencing method (scFAST-seq), which combines semi-random primers with high reverse transcription efficiency, template switching and convenient rRNA removal methods, allowing the construction of full-length RNA libraries of up to 12,000 cells within 8 hours. Compared to regular 3 scRNA-seq, scFAST-seq has similar sensitivity to mRNA detection, sequencing cost and experimental workflow. Moreover, scFAST-seq has clear advantages in detecting non-polyadenylated transcripts, covering longer transcript length, and identifying more splice junctions. In addition, scFAST-seq can more accurately predict the direction of cell differentiation by calculating RNA velocity. Furthermore, we demonstrated that scFAST-seq combined with target region enrichment can simultaneously identify somatic mutations and cellular status of individual tumor cells, which is valuable information for precision medicine.

genomics↗

bioRxiv2023

The commensal protist Tritrichomonas musculus exhibits a dynamic life cycle that induces B cell-modulated remodeling of the gut microbiota

Commensal protists and gut bacterial communities exhibit complex relationships, mediated at least in part through host immunity. To improve our understanding of this tripartite interplay, we investigated community and functional dynamics between the murine protist Tritrichomonas musculus (T. mu) and intestinal bacteria in healthy and B cell-deficient mice. We identified dramatic, protist-driven remodeling of resident microbiome growth and activities, in parallel with T. mu functional changes, accelerated in the absence of B cells. Metatranscriptomic data revealed nutrient-based competition between bacteria and the protist. Single cell transcriptomics identified distinct T. mu life stages, providing new evidence for trichomonad sexual replication and the formation of pseudocysts. Unique cell states were validated in situ through microscopy and flow cytometry. Our results reveal complex microbial dynamics during the establishment of a commensal protist in the gut, and provide valuable datasets to drive future mechanistic studies.

microbiology↗

bioRxiv2023

Extraction of biological signals by factorization enables the reliable analysis of single-cell transcriptomics

Accurately and reliably capturing actual biological signals from single-cell transcriptomics is vital for achieving legitimate scientific results, which is unfortunately hindered by the presence of various kinds of unwanted variations. Here we described a deep auto-regressive factor model known as scPhenoXMBD, demonstrated that each genes expression can be split into discrete components that represent biological signals and unwanted variations, which effectively mitigated the effects of unwanted variations in the data of single-cell sequencing. Using scPhenoXMBD, we evaluated various factors affecting IFN {beta} -stimulated immune cells and demonstrated that biological signal extraction facilitates the identification of IFN{beta}-responsive pathways and genes. Numerous experiments were conducted to show that scPhenoXMBD could be utilized successfully in enhancing cell clustering stability, obtaining identical cell populations from diverse data sources, advancing the single-cell CRISPR screening of functional elements, and minimizing the influence of inter-subject discrepancies in the cell-disease relationships. scPhenoXMBD is anticipated to be a dependable and repeatable method for the precise analysis of single-cell data.

bioinformatics↗

bioRxiv2023

Serum albumin maintains Wnt water-solubility and activity

Wnt proteins regulate adult tissue homeostasis and repair by driving stem cell self-renewal and differentiation. High-performance Wnt preparations have enormous therapeutic potential, especially alongside various stem cell technologies. Currently, most of these Wnt preparations contain FBS or the detergent CHAPS to maintain Wnt solubility and activity. Recently, afamin was identified as a serum factor that solubilizes Wnt3a in conditioned media (CM), obviating the requirement for animal sera. Here, we report serum albumin (SA) is required for afamin-mediated solubilization of Wnt3a in CM. Moreover, SA-mediated solubilization of purified Wnt3a in tubes does not require afamin. This means conventional CHAPS-Wnt3a preparations can be modified into SA-purified Wnt3a (SA-pWnt3a) preparations by exchanging CHAPS for SA through dialysis. SA-pWnt3a preparations effectively promote the growth of human stem cell organoids. These data suggest SA as a physiological factor for maintaining Wnt3a activity in therapeutic applications.

biochemistry↗

bioRxiv2022

Deep learning-based predictive identification of functional subpopulations of hematopoietic stem cells and multipotent progenitors

Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) are crucial for maintaining lifelong hematopoiesis. Developing methods to distinguish stem cells from other progenitors and evaluate stem cell functions has been a central task in stem cell research. Deep learning has been demonstrated as a powerful tool in cell image analysis and classification. In this study, we explored the possibility of using deep learning to differentiate HSCs and MPPs based on their light microscopy (DIC) images. After extensive training and validation with large image data sets, we successfully develop a three-class classifier (we named it the LSM model) that reliably differentiate long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. Importantly, we demonstrated that our LSM model achieved its differentiating capability by learning the intrinsic morphological features from cell images. Furthermore, we showed that the performance of our LSM model was not affected by how these cells were identified and isolated, i.e., sorted by surface markers or intracellular GFP markers. Prospective identification of HSCs and MPPs in Evi1GFP transgenic mice by LSM model suggested that the cells with the highest GFP expression were LT-HSCs, and this prediction was substantiated later by a long-term competitive reconstitution assay. Moreover, based on DIC image data sets, we also successfully built another two-class classifier that can effectively distinguish aged HSCs from young HSCs, which both express the same surface markers but are functionally different. This finding is of particular interest since it may provide a novel quick and efficient approach, obviating the need for a time-consuming transplantation experiment, to evaluate the functional states of HSCs. Together, our study provides evidence for the first time that HSCs and MPPs can be differentiated by deep learning based on cell morphology. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into molecular mechanisms underlying the self-renewal feature of stem cells.

bioengineering↗

bioRxiv2022

Label-free and high-throughput removal of residual undifferentiated cells from iPSC-derived spinal-cord progenitor cells

The transplantation of spinal cord progenitor cells (SCPCs) derived from human induced pluripotent stem cells (iPSCs) has beneficial effects on treating spinal cord injury (SCI). However, the presence of residual undifferentiated iPSCs amongst their differentiated progeny poses a high risk as it can develop teratomas or other types of tumors post-transplantation. Despite the need to remove these residual undifferentiated iPSCs, no specific surface markers can identify them for subsequent removal. By profiling the size of SCPCs after a 10-day differentiation process, we found that the large-sized group contains significantly more cells expressing pluripotent markers. In this study, we employed a sized-based, label-free separation using an inertial microfluidic-based device to remove tumor-risk cells. The device can reduce the number of undifferentiated cells from an SCPC population with high throughput (i.e., > 3 million cells per minute) without affecting cell viability and functions. The sorted cells were verified with immunofluorescence staining, flow cytometry analysis, and colony culture assay. We demonstrated the capabilities of our technology to reduce the percentage of OCT4-positive cells. Our technology has great potential for the downstream processing of cell manufacturing workflow, ensuring better quality and safety of transplanted cells.

bioengineering↗

bioRxiv2022

Neurodegeneration Risk Factor, EIF2AK3 (PERK), Influences Tau Protein Aggregation

Tauopathies are neurodegenerative diseases caused by pathologic misfolded tau protein aggregation in the nervous system. Population studies implicate EIF2AK3 (eukaryotic translation initiation factor 2 alpha kinase 3), better known as PERK (protein kinase R-like endoplasmic reticulum kinase), as a genetic risk factor in several tauopathies. PERK is a key regulator of intracellular proteostatic mechanisms - Unfolded Protein Response (UPR) and Integrated Stress Response (ISR). Previous studies found that tauopathy-associated PERK variants encoded functional hypomorphs with reduced signaling in vitro. But, it remained unclear how altered PERK activity led to tauopathy. Here, we chemically or genetically modulated PERK signaling in cell culture models of tau aggregation and found that PERK pathway activation prevented tau aggregation while inhibition exacerbated tau aggregation. In primary tauopathy patient brain tissues, we found that reduced PERK signaling correlated with increased tau neuropathology. We found that tauopathy-associated PERK variants targeted the ER luminal domain; and two of these variants damaged hydrogen bond formation. Our studies support that PERK activity protects against tau aggregation and pathology. This may explain why people carrying hypomorphic PERK variants have increased risk for developing tauopathies. Finally, our studies identify small molecule augmentation of PERK signaling as an attractive therapeutic strategy to treat tauopathies by preventing tau pathology.

neuroscience↗

bioRxiv2022

A unified classification system for HIV-1 5' long terminal repeats

The HIV-1 provirus mainly consists of internal coding region flanked by the 2 same long terminal repeats (LTRs) at each terminus. The LTRs play important roles in HIV-1 reverse transcription, integration, and transcription by the association with host factors. However, despite of the significant study advances of the internal coding regions of HIV-1 by using definite reference classification, there are no systematic classifications for HIV-1 5 LTRs, which hinders our elaboration on 5 LTR and a better understanding of the viral origin, spread and therapy. Here, by analyzing all available resources of 5 LTR sequences in public databases following 4 recognized principles for the reference classification, 83 representatives and 14 consensus sequences were identified as representatives of 2 groups, 6 subtypes, 6 sub-subtypes, and 9 CRFs. To test the reliability of our established classification system, the constructed references were applied to identify the 5 LTR assignment of the 22 clinical isolates in China. The results revealed that 16 out of 22 tested strains showed a consistent subtype classification with the previous LTR-independent classification system. However, 6 strains, for which recombination events within 5 LTR were demonstrated, unexpectedly showed a different subtype classification, leading a significant change of binding sites for important transcription factors including SP1, p53, and NF-{kappa}B. The binding change of these transcriptional factors would probably affect the transcriptional activity of 5 LTR. This study established a unified classification system for HIV-1 5 LTRs, which will facilitate HIV-1 characterization and be helpful for both basic and clinical research fields. IMPORTANCEHere, a scientific, reliable, and usable classification system based on the 5 LTR sequences was established, which will allow us to effectively facilitate the precise typing of HIV-1 strains. This classification system was applied to 22 HIV-1 strains circulating in China, we found that 6 out of 22 strains analyzed, belonged to a different subtype when our results were compared to those obtained with the previous LTR-independent classification system. Thus, these data demonstrated that our classification method could greatly improve the HIV-1 subtype classification. We found that 6 5 LTR sequences showed recombination events, leading to a significant exchange of the binding sites of transcriptional factors. Thus, this work established a comprehensive HIV-1 5 LTR classification system, which will help the scientific community to precisely characterize HIV-1 variants, and better understand the origin and spread of HIV-1 strains, and it also may be helpful for pathogenicity and transmissibility evaluation studies.

microbiology↗

bioRxiv2022

The Neural Correlates of Arousal: The Ventral Posterolateral Nucleus-Global Transient Co-Activation

Arousal and awareness are two components of consciousness whose the neural mechanisms remain unclear. Spontaneous increases of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal has been found to be sensitive to changes in arousal. By contrasting BOLD datasets with altered arousal levels, we found that the activation of ventral posterolateral nucleus (VPL) decreased during transient increase in the global signal (top 17% data) in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) as compared to wakefulness, and even in eye-closed (compared with eyes-open) in healthy awake-states, while this activation remained unchanged in patients with unresponsive wakefulness syndrome characterized by high arousal without awareness. These results demonstrate that co-activation of the VPL and global activity is critical to arousal, but not to awareness. One-Sentence SummaryThe VPL nucleus-global brain transient co-activation is related to physiological arousal but not to perceptual awareness.

neuroscience↗

bioRxiv2022

A novel method revealing animal evolutionary relationships based on milk Mid-infrared

Mid-infrared spectra (MIRS) can effectively reflect the chemical bonds in milk, which has been widely used in dairy herd improvement. However, the relationship between MIRS and animal evolution remains largely unclear. This study firstly found great differences in MIRS and the components of milk by analyzing MIRS information of 12 different mammal species. A five-level discriminant model of evolutionary level based on MIRS was established with a test set kappa coefficient >0.97. In addition, a regression model of genetic distance was also established to estimate the genetic distance of different animal species with a correlation coefficient of R >0.94. These results showed that this method could be used for accurate mammalian evolutionary relationship assessment. We further clarified the potential relationship between MIRS and genes, such as PPP3CA and SCD that could change MIRS by regulating specific milk components. In conclusion, we expand the application of MIRS in animal species identification and evolution research and provide new perspectives for the research on the formation mechanism of different animal milk special components.

evolutionary biology↗

bioRxiv2022

inMTSCCA: An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omics Brain Imaging Genetics

Identifying genetic risk factors for Alzheimers disease (AD) is an important research topic. To date, different endophenotypes such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes have shown the great value in uncovering risk genes compared to case-control studies. Biologically, a co-varying pattern of these different omics derived endophenotypes could result from the shared genetic basis. However, existing methods mainly focus on the effect of endophenotypes, and that of cross-endophenotype associations remains largely unexploited. In this paper, we used both endophenotypes and their cross-associations of multi-omics to identify genetic risk factors, and proposed two integrated multi-task sparse canonical correlation analysis (MTSCCA) methods, i.e., pairwise endophenotype correlation guided MTSCCA (pcMTSCCA) and high-order endophenotype correlation guided MTSCCA (hocMTSCCA). pcMTSCCA employed pairwise correlations between MRI-derived, plasma-derived, and cerebrospinal fluid (CSF) derived endophenotypes as an additional penalty. hocMTSCCA used high-order correlations among these multi-omics for regularization. To figure out genetic risk factors at individual and group levels, as well as altered endophenotypic markers, we introduced sparsity-inducing penalties in both models. We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation data and real neuroimaging, proteomic analytes, and genetic data. The results showed that our methods obtained better or comparable canonical correlation coefficients (CCCs) and feature subsets than benchmarks. Most importantly, the identified genetic loci and heterogeneous endophenotypic markers showed high relevance. Therefore, jointly using multi-omics endophenotypes and their cross-endophenotype associations are promising to reveal genetic risk factors, and both methods are qualified for this complicated task. The source code and manual of inMTSCCA is available at: https://ngdc.cncb.ac.cn/biocode/tools/BT007330.

bioinformatics↗