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Tran, B.

Publications and source records attributed to Tran, B..

At least 19 recordsLinked to original sources

Single-cell full-length transcriptome of human lung reveals genetic effects on isoform regulation beyond gene-level expression

Genetic regulation of splicing uniquely contributes to trait-associated genome-wide association studies (GWAS) signals. However, quantitative trait loci (QTL) analysis using short-read sequencing of bulk tissues fails to capture full-length and cell-type-specific isoforms. Here, we present an isoform-level lung cell atlas from 129 never-smoking Korean women using single-cell long-read RNA-sequencing, identifying abundant unannotated and cell-type-specific isoforms. Isoform-level signatures of 37 lung cell types display a larger difference and therefore improve cell-type classification compared to gene-level expression. Notably, isoform-QTLs (isoQTLs) detect unannotated and/or cell-type-specific isoforms with independent genetic regulation from expression-QTL (eQTL), supported by enriched splicing functional elements. IsoQTLs nominate susceptibility isoforms from previously unexplained lung function and cancer GWAS loci, via eQTL-independent signals. We highlight a potentially functional novel variant of PPIL6 in multiciliated cells underlying lung cancer risk through alternative splicing. This isoform-level resource advances our understanding of cell-type-specific isoform regulation and its contribution to lung traits and diseases.

genetics↗

High-throughput quantification of huntingtin mRNA expression and aggregation in mouse brain using automated RNAscope imaging.

Huntingtons disease (HD) is a repeat-associated neurodegenerative disorder traditionally characterized by toxic protein pathology resulting from expanded CAG repeats in the huntingtin (HTT) gene. In recent years, however, studies have identified repeat expansion-driven RNA pathology as an additional and potentially independent contributor to disease. In particular, mutant HTT transcripts containing expanded CAG repeats accumulate in the nucleus and form discrete RNA clusters, a feature shared with several other repeat-associated disorders. While protein aggregation and its downstream consequences have been extensively studied, our current understanding of the composition, organization, and dynamics of these nuclear mRNA clusters remains limited. Progress in this area has been constrained in part by the lack of robust methods to detect and quantify expanded HTT transcripts at single-molecule resolution within intact tissue. As a result, the contribution of RNA clustering to disease mechanisms, its relationship to repeat length, and its interaction with other pathological features of HD remain poorly defined. Here we present a high-throughput RNAscope pipeline that combines automated confocal imaging with rigorous microscope characterization to quantify both single mRNA molecules and multi-transcript clusters in fixed mouse brain tissue. Using 3D Gaussian point-spread function (PSF) fitting calibrated on 200 nm fluorescent beads and pointilistic image features from tissue data, we establish per-slide intensity thresholds from negative controls and normalize experimental signals to single-molecule reference intensities. The critical validation of our approach operates at two scales: for single molecules, the linear relationship between spot size and intensity (r2 > 0.90) reflects variable probe binding along transcripts; for clusters, the linear scaling between cluster volume and mRNA content (R2 > 0.98) confirms uniform probe accessibility and enables quantitative conversion of fluorescence intensity to absolute mRNA counts. Applied to HttQ111+/- knock-in mice across multiple ages, we analyzed thousands of fields of view (FOVs), detecting >900,000 single mRNA molecules and segmenting >1.9 million mRNA clusters using two probes targeting mouse huntingtin (Htt): one detecting the spliced transcript that uses early cryptic polyadenylation sites in intron 1 (HTT1a), and one detecting full-length Htt (fl-HTT). Our analysis reveals considerable heterogeneity in mRNA accumulation: 16-63% of Q111 FOVs are classified as "extreme" (exceeding the 95th percentile of wildtype clustered mRNA levels), with striatum showing higher prevalence than cortex for both probes (HTT1a: 63% striatum, 31% cortex; fl-HTT: 44% striatum, 16%cortex). Extreme FOVs are characterized by elevated cluster numbers (2-6x more clusters per nucleus) and higher cluster density (1.3-1.7x more mRNA per {micro}m3). Cluster localization shows nuclear bias ([~]68%) in normal FOVs, but extreme FOVs exhibit a shift toward cytoplasmic localization, particularly for fl-HTT (48% nuclear vs 68% in normal FOVs), though the interpretation of this shift requires further investigation. Despite the large dataset at the cellular level, our study included only 11 mice (9 Q111, 2 wildtype), and this limited sample size precluded robust statistical inference at the animal level. Nevertheless, these quantitative metrics provide a framework for investigating disease mechanisms and evaluating therapeutic interventions using RNAscope in future studies with larger cohorts.

cell biology↗

Natural killer cell TGF- signaling regulates senolytic activity and vascular patterning in the postnatal lung

BackgroundBronchopulmonary dysplasia (BPD) is a disease of neonatal lung development that is linked to impaired pulmonary vascularization, dysregulated transforming growth factor-{beta} (TGF-{beta}) signaling and the accumulation of senescent cells. Despite the established role for TGF-{beta} signaling in promoting vascular remodeling and suppressing the senolytic activity of natural killer (NK) cells, the contribution of NK cell TGF-{beta} signaling to postnatal lung patterning and the pathogenesis of BPD remains unclear. MethodsMice bearing an NK cell-selective deletion of the type-II TGF-{beta} receptor (Tgfbr2NK-/-) were analyzed for vascular and alveolar structure, lung NK cell infiltration, senescence markers and lung function testing across neonatal and adult timepoints. Single-cell RNA sequencing of lung tissue from both neonatal mice and human infants with BPD was performed. The effect of enhanced NK cell activity in a hyperoxia-induced model of BPD was assessed in Tgfbr2NK-/-neonates, as well as pharmacologically, using the TGF-{beta} ligand trap/IL-15 superagonist, HCW9218. ResultsNeonatal Tgfbr2NK-/- mice exhibited a baseline reduction in distal arteriolar density, impaired alveolarization, and sex-specific deficits in long-term lung function. Single-cell RNA sequencing identified the excessive clearance of senescent endothelial cells by TGF-{beta} insensitive NK cells in the lungs of Tgfbr2NK-/- neonates, which served as a contributor of the BPD-like phenotype observed in naive animals. Tgfbr2NK-/- mice were protected from impaired lung development in the hyperoxia model. Sequencing from lung tissue from infants with BPD confirmed excessive TGF-{beta} signaling and cytotoxic impairment in NK cells. Treatment with HCW9218 prevented senescent cell accumulation and rescued lung development in the hyperoxia mouse model. ConclusionsThese findings identify TGF-{beta} as a tunable regulator of NK cell senolytic activity that is essential to normal postnatal lung development. Excessive NK cell TGF-{beta} signaling contributes to impaired lung development following exposure to neonatal hyperoxia and may serve as a viable therapeutic target for human BPD.

developmental biology↗

Long-read sequencing of single cell-derived melanoma subclones reveals divergent and parallel genomic and epigenomic evolutionary trajectories

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide vari- ants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alter- ations due to inherent technical limitations. To overcome that, here we introduce an approach for long-read sequencing of single-cell derived subclones, and use it to profile 23 subclones of a mouse melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational frame- work for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by indepen- dent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development of new computational methods.

genomics↗

Machine Learning Guided Structure Activity Discovery of Polymer Configurations in Lipid Nanoparticles for Kiss-and-Run Endosomal Escape

Endosomal escape remains a major barrier to effective nucleic acid delivery via lipid nanoparticles (LNPs). Here, we address this challenge by incorporating a pH-sensitive polymer, polyhistidine, into LNPs (pLNPs) to facilitate endosomal escape, with a focus on optimizing the polymers molecular weight (MW) and configuration--parameters that remain largely unexplored. Through systematic engineering, we designed linear and branched polyhistidine architectures with varied MWs and configurations. In vivo screening identified an optimized pLNP formulation incorporating a symmetrical bis-lysine histidine dendron with a MW of [~]1800 g/mol, which achieved a 266-fold increase in liver bioluminescence following intravenous delivery of luciferase mRNA compared to standard LNPs at an equivalent RNA dose. Mechanistic studies revealed that polymer configuration within pLNPs is critical for eliciting the proton sponge effect, leading to osmotic swelling and endosomal rupture. This configuration also promoted rapid endosomal membrane destabilization via a kiss-and-run mechanism, enabling efficient cytosolic release. When delivering base editor mRNA and single-guide RNA, the optimized pLNPs achieved 8% gene editing efficiency in the mouse liver at a low dose of 0.1 mg/kg, compared to 1% with standard LNPs. To accelerate discovery and address macromolecular design challenges, we developed a machine learning (ML) framework based on amino acid-level graph neural networks (GNNs). This approach identified branched, dendritic configurations with densely arranged histidine residues on a multivalent core as key determinants of delivery performance. The top ML-predicted candidate, NS535, achieved a 705-fold increase in liver bioluminescence over standard LNPs, validating our data-driven design strategy. Together, these findings establish a closed-loop platform integrating rational design, mechanistic validation, and ML-guided optimization to advance RNA delivery. By elucidating structure-activity relationships for polyhistidine carriers and demonstrating efficient, low-dose genome editing, this work provides a blueprint for next-generation nucleic acid therapeutics.

bioengineering↗

Ultra-Fast Multi-Organ Proteomics Unveils Tissue-Specific Mechanisms of Drug Efficacy and Toxicity

Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present UFP-MS, an ultra-fast mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. Our streamlined sample preparation workflow features high-throughput homogenization, adaptive focused acoustics (AFA)-assisted proteolysis, and Evotip-accelerated desalting, allowing for the processing of up to 96 tissue samples in 5 h. As a practical application, we analyzed 507 samples from 13 mouse tissues treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the UFP-MS method greatly accelerates the generation of biological insights and clinically actionable hypotheses into tissue-specific vulnerabilities targeted by ASNase.

systems biology↗

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

bioinformatics↗

Sequential emergence and contraction of epithelial subtypes in the prenatal human choroid plexus revealed by a stem cell model

Despite the major roles of choroid plexus epithelial cells (CPECs) in brain homeostasis and repair, their developmental lineage and diversity remain undefined. In simplified differentiations from human pluripotent stem cells, derived CPECs (dCPECs) displayed canonical properties and dynamic multiciliated phenotypes that interacted with A{beta} uptake. Single dCPEC transcriptomes over time correlated well with human organoid and fetal CPECs, while pseudotemporal and cell cycle analyses highlighted the direct CPEC origin from neuroepithelial cells. In addition, time series analyses defined metabolic (type 1) and ciliogenic dCPECs (type 2) at early timepoints, followed by type 1 diversification into anabolic-secretory (type 1a) and catabolic-absorptive subtypes (type 1b) as type 2 cells contracted. These temporal patterns were then confirmed in independent derivations and mapped to prenatal stages using human tissues. In addition to defining the prenatal lineage of human CPECs, these findings suggest new dynamic models of ChP support for the developing human brain.

neuroscience↗

Use of Stem Cell-Derived Cardiomyocyte and Nasal Epithelium Models to Establish a Multi-Tissue Model Platform to Validate Repurposed Drugs Against SARS-CoV-2 Infection

The novel coronavirus disease (COVID-19) and any future coronavirus outbreaks will require more affordable, effective and safe treatment options to complement current ones such as Paxlovid. Drug repurposing can be a promising approach if we are able to find a rapid, robust and reliable way to down-select and screen candidates using in silico and in vitro approaches. With repurposed drugs, ex vivo models could offer a rigorous route to human clinical trials with less time invested into nonclinical animal (in vivo) studies. We have previously shown the value of commercially available ex vivo/3D airway and alveolar tissue models, and this paper takes this further by developing and validating human nasal epithelial model and embryonic stem cells derived cardiomyocyte model. Five shortlisted candidates (fluvoxamine, everolimus, pyrimethamine, aprepitant and sirolimus) were successfully compared with three control drugs (remdesivir, molnupiravir, nirmatrelvir) when tested against key variants of the SARS-CoV-2 virus including Delta and Omicron, and we were able to reconfirm our earlier finding that fluvoxamine can induce antiviral efficacy in combination with other drugs. Scalability of this high-throughput screening approach has been demonstrated using a liquid handling robotic platform for future Disease-X outbreaks.

microbiology↗

Trabid patient mutations impede the axonal trafficking of adenomatous polyposis coli to disrupt neurite growth

Trabid/ZRANB1 missense mutations have been identified in children diagnosed with a range of congenital disorders including reduced brain size, but how Trabid regulates neurodevelopment is not understood. We have characterised these patient mutations in cells and mice to identify a key role for Trabid in the regulation of neurite growth. One of the patient mutations flanked the catalytic cysteine of Trabid and its deubiquitylating (DUB) activity was abrogated. The second variant retained DUB activity, but failed to bind STRIPAK, a large multiprotein assembly implicated in cytoskeleton organisation and neural development. Trabid/ZRANB1 knock-in mice harbouring either of these patient mutations exhibited reduced neuronal and glial cell densities in the brain and a motor deficit consistent with fewer dopaminergic neurons and projections. Mechanistically, both DUB-impaired and STRIPAK-binding-deficient Trabid variants impeded the trafficking of adenomatous polyposis coli (APC) to microtubule plus-ends. Consequently, the formation of neuronal growth cones and the trajectory of neurite outgrowth from mutant midbrain progenitors were severely compromised. We propose that STRIPAK recruits Trabid to deubiquitylate APC, and that in cells with mutant Trabid, APC becomes hyperubiquitylated and mislocalised causing impaired organisation of the cytoskeleton that underlie the neuronal and developmental phenotypes.

cell biology↗

Exploiting the therapeutic vulnerability of IDH-mutant gliomas with zotiraciclib

Isocitrate dehydrogenase (IDH)-mutant gliomas have distinctive metabolic and biological traits that may render them susceptible to targeted treatments. Here, by conducting a high-throughput drug screen, we pinpointed a specific susceptibility of IDH-mutant gliomas to zotiraciclib (ZTR). ZTR exhibited selective growth inhibition across multiple IDH-mutant glioma in vitro and in vivo models. Mechanistically, ZTR at low doses suppressed CDK9 and RNA Pol II phosphorylation in IDH-mutant cells, disrupting mitochondrial function and NAD+ production, causing oxidative stress. Integrated biochemical profiling of ZTR kinase targets and transcriptomics unveiled that ZTR-induced bioenergetic failure was linked to the suppression of PIM kinase activity. We posit that the combination of mitochondrial dysfunction and an inability to adapt to oxidative stress resulted in significant cell death upon ZTR treatment, ultimately increasing the therapeutic vulnerability of IDH-mutant gliomas. These findings prompted a clinical trial evaluating ZTR in IDH-mutant gliomas towards precision medicine (NCT05588141). HighlightsO_LIZotiraciclib (ZTR), a CDK9 inhibitor, hinders IDH-mutant glioma growth in vitro and in vivo. C_LIO_LIZTR halts cell cycle, disrupts respiration, and induces oxidative stress in IDH-mutant cells. C_LIO_LIZTR unexpectedly inhibits PIM kinases, impacting mitochondria and causing bioenergetic failure. C_LIO_LIThese findings led to the clinical trial NCT 05588141, evaluating ZTR for IDH-mutant gliomas. C_LI

cancer biology↗

Uncovering strain- and age- dependent differences in innate immune response to SARS-CoV-2 infection in nasal epithelia using combined short and long-read scRNA-seq

Assessing the impact of SARS-CoV-2 variants on the host is crucial with continuous emergence of new variants. We employed single-cell sequencing to investigate host transcriptomic response to ancestral and Alpha-strain SARS-CoV-2 infections within air-liquid-interface human nasal epithelial cells from adults and adolescents. Strong innate immune responses were observed across lowly-infected and bystander cell-types, and heightened in Alpha-infection. Contrastingly, the innate immune response of highly-infected cells was like mock-control cells. Alpha highly-infected cells showed increased expression of protein refolding genes compared with ancestral-strain-infected adolescent cells. Oxidative phosphorylation- and translation-related genes were down-regulated in bystander cells versus infected and mock-control cells, suggesting that the down-regulation is protective and up-regulation supports viral activity. Infected adult cells revealed up-regulation of these pathways compared with infected adolescents, implying enhanced pro-viral states in infected adults. Overall, this highlights the complexity of cell-type-, age- and viral-strain-dependent host epithelial responses to SARS-CoV-2 and the value of air-liquid-interface cultures.

immunology↗

UGDH promotes tumor-initiating cells and a fibroinflammatory tumor microenvironment in ovarian cancer.

Epithelial ovarian cancer (EOC) is a global health burden, with the poorest five-year survival rate of the gynecological malignancies due to diagnosis at advanced stage and high recurrence rate. Recurrence in EOC is driven by the survival of chemoresistant, stem-like tumor-initiating cells (TICs) that are supported by a complex extracellular matrix (ECM) and immunosuppressive microenvironment. To target TICs to prevent recurrence, we identified genes critical for TIC viability from a whole genome siRNA screen. A top hit was the cancer-associated, proteoglycan subunit synthesis enzyme UDP-glucose dehydrogenase (UGDH). Immunohistochemistry was used to delineate UGDH expression in histological and molecular subtypes of EOC. High UGDH expression was observed in the majority of high-grade serous ovarian cancers with variable expression in clear cell, mucinous and endometrioid histotypes. A distinctive prognostic difference was revealed when serous cancers were stratified by molecular subtype, where high UGDH was associated with poor prognosis in the C1/Mesenchymal subtype and low UGDH was associated with poor prognosis in the C4/Differentiated subtype. Ovarian cancer cell lines were subtyped according to the molecular subtypes, and we examined the effect of modulating UGDH expression in cell lines representing the C1/Mesenchymal subtype and C4/Differentiated subtypes. Knockdown of UGDH in the C1/Mesenchymal subtype reduced spheroid viability, sphere-formation and the CD133+/ALDH high TIC population. Conversely, overexpression of UGDH in the differentiated subtype enhanced spheroid formation but reduced the TIC population. Inflammatory cytokine expression was altered by UGDH expression. In co-culture models, altering UGDH expression in spheroids affected the gene expression of mesothelial cells causing changes to matrix remodeling proteins. The effect of UGDH knockdown or overexpression in the C1/Mesenchymal and C4/Differentiated subtypes, respectively, was tested on mouse intrabursal xenografts and showed dynamic changes to the tumor stroma. Knockdown of UGDH reduced tumor burden in C1/Mesenchymal xenografts compared to controls. These data show that modulation of UGDH expression in tumors influences cells in the microenvironment and reveals distinct roles for UGDH in the mesenchymal and differentiated molecular subtypes of EOC. UGDH is a potential therapeutic target in TICs, for the treatment of metastatic and recurrent EOC, particularly in patients with the mesenchymal molecular subtype.

cancer biology↗

Transient, developmental exposure to patient-derived anti-NMDA receptor autoantibodies causes long-term axonal and behavioral defects

NMDA receptor mediated autoimmune encephalitis (NMDAR-AE) frequently results in persistent sensory-motor deficits, especially in children, yet the underlying mechanisms remain unclear. This study investigated the long-term effects of exposure to a patient-derived GluN1-specific monoclonal antibody (mAb) during a critical developmental period (from postnatal day 3 to day 12) in mice. We observed long-lasting sensory-motor deficits characteristic of NMDAR-AE, along with permanent changes in callosal axons within the primary somatosensory cortex (S1) in adulthood, including increased terminal branch complexity. This complexity was associated with paroxysmal recruitment of neurons in S1 in response to callosal stimulation. Particularly during complex motor tasks, mAb3-treated mice exhibited significantly reduced inter-hemispheric functional connectivity between S1 regions, consistent with pronounced sensory-motor behavioral deficits. These findings suggest that transient exposure to anti-GluN1 mAb during a critical developmental window may lead to irreversible morphological and functional changes in callosal axons, which could significantly impair sensory-motor integration and contribute to long-lasting sensory-motor deficits. Our study establishes a new model of NMDAR-AE and identifies novel cellular and network-level mechanisms underlying persistent sensory-motor deficits in this context. These insights lay the foundation for future research into molecular mechanisms and the development of targeted therapeutic interventions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/510196v3_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@37f452org.highwire.dtl.DTLVardef@1894e46org.highwire.dtl.DTLVardef@171f954org.highwire.dtl.DTLVardef@1a81742_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Structure-guided bifunctional molecules hit a DEUBAD-lacking hRpn13 species upregulated in multiple myeloma

Proteasome substrate receptor hRpn13 is a promising anti-cancer target. By integrated in silico and biophysical screening, we identified a chemical scaffold that binds hRpn13 with non-covalent interactions that mimic the proteasome and a weak electrophile for Michael addition. hRpn13 Pru domain binds proteasomes and ubiquitin whereas its DEUBAD domain binds deubiquitinating enzyme UCHL5. NMR revealed lead compound XL5 to interdigitate into a hydrophobic pocket created by lateral movement of a Pru {beta}-hairpin with an exposed end for Proteolysis Targeting Chimeras (PROTACs). Implementing XL5-PROTACs as chemical probes identified a DEUBAD-lacking hRpn13 species (hRpn13Pru) present naturally with cell type-dependent abundance. XL5-PROTACs preferentially target hRpn13Pru, causing its ubiquitination. Gene-editing and rescue experiments established hRpn13 requirement for XL5-PROTAC-triggered apoptosis and increased p62 levels. These data establish hRpn13 as an anti-cancer target for multiple myeloma and introduce an hRpn13-targeting scaffold that can be optimized for preclinical trials against hRpn13Pru-producing cancer types.

molecular biology↗

Whole Genome and Exome Sequencing Reference Datasets from A Multi-center and Cross-platform Benchmark Study

With the rapid advancement of sequencing technologies in the past decade, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from errors or artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically benchmarking sequencing platforms, library protocols, bioinformatics pipelines and for measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples, a human triple-negative breast cancer cell line and a matched normal cell line derived from B lymphocytes. We generated whole-genome (WGS) and whole-exome sequencing (WES) data using 16 NGS library preparation protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the paired reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

bioinformatics↗

A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset

Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmarking scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as a mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Our benchmark datasets provide a resource that we believe will have great value for the single-cell community by serving as a reference dataset for evaluating various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

bioinformatics↗

Single-cell atlas of tumor clonal evolution in liver cancer

Tumor evolution is a key feature of tumorigenesis and plays a pivotal role in driving intratumor heterogeneity, treatment failure and patients prognosis. Here we performed single-cell transcriptome profiling of 46 primary liver cancers from 37 patients enrolled for interventional studies. We surveyed the landscape of ~57,000 malignant and non-malignant cells and determined tumor cell clonality by developing a machine learning-based consensus clustering method. We found evidence of tumor cell branching evolution using hierarchical clustering, RNA velocity as well as reverse graph embedding methods. Interestingly, an increasing tumor cell clonality was tightly linked to patients prognosis, accompanied by a polarized immune cell landscape. We identified osteopontin as a key player for tumor cell evolution and microenvironmental reprogramming. Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers for tumor evolution in response to therapy.

cancer biology↗