Biology subjects

Tang, N. L.-s.

Publications and source records attributed to Tang, N. L.-s..

7 recordsLinked to original sources

bioRxiv2026

In-silico cell sorting revealed granulocyte-specific single-cell-type gene expression from peripheral blood bulk expression data and its application as host response biomarkers to discriminate bacterial and viral infections

Peripheral Blood transcriptome analysis evaluated the bulk transcript abundance (TA) covering all leukocyte cell populations. However, there are 2 main problems in using bulk expression as biomarkers: (1) A long list of differential expression genes (DEGs) was found, and (2) DEGs cannot be attributed to a host response of any specific cell-type. TA assays after conventional cell sorting, as the gold-standard method, is too tedious for routine use. Recently, we showed that by using a ratio-based biomarker, RBB (ratio of two stringently selected genes), it is feasible to interrogate the gene expression of a single cell-type (monocyte and B lymphocyte) in peripheral whole blood (WB) directly. Here, we apply this in-silico cell sorting algorithm (DIRECT LS-TA, Direct Leukocyte Single cell-type Transcript Abundance) to granulocytes in WB samples to reveal RBBs specific to granulocytes. This DIRECT LS-TA approach without the need for cell-sorting was applied to public datasets to differentiate the 2 types of infection (bacterial vs viral infection). The following RBBs measured in WB correlate with the expression of target (numerator) genes in purified granulocytes, thus cell-sorting can be avoided by using these RBBs: ARG1/SRGN, ANXA3/SRGN, RSAD2/SRGN. Together with monocyte DIRECT LS-TA biomarkers, IFI27/PSAP, direct quantification of 4 genes provided optimal differentiation of viral from bacterial infection. Meta-analysis and unsupervised machine learning classification confirmed the superior performance of DIRECT LS-TA biomarkers. These RBBs found by prior In-silico cell-sorting identified pairs of genes that are used to formulate as ratio-based biomarkers (RBBs) to represent gene expression of granulocytes inside whole blood cell-mixture samples which was useful to triage febrile patients into two major categories of febrile diseases between viral and bacterial infection with high degree of sensitivity and specificity.

immunology↗

bioRxiv2025

A map of Non-translated RNA (nt-RNA) junctions in cancer genomes: a database resource of unproductive splicing

BackgroundNon-translated transcripts (nt-RNAs) with frame-shifts or premature termination codons resulting from alternative splicing events (ASE), have been recently found at unexpectedly abundant in transcriptomes of cancer tissue. However, their full genomic spectrum has not yet been fully elucidated. This study comprehensively characterised the expression of signature junctions of these nt-RNA (termed "toxic junctions" here) of both known and novel nt-RNA across multiple cancer types and investigated their potential as biomarkers. MethodsRNA-seq data of [~]6,000 samples, including the tumor and normal samples for 13 cancer types were retrieved from The Cancer Genome Atlas database (TCGA) together with data from Cancer Cell Line Encyclopedia (CCLE) project. Due to the difficulty in quantifying the entire transcript isoform of nt-RNA, we pioneered an algorithm to focus exclusively on the expression of junctional reads, which also circumvented the limitation of non-directional RNA- seq of TCGA data. We showed that the majority of nt-RNA is associated with at least one toxic junction. We built a comprehensive catalogue of known nt-RNA toxic junctions from genome databases. And novel toxic junctions were also identified by a new junction-focused algorithm from the higher quality discovery subsets of TCGA data. Splicing in Ratio (SiR) was used to quantify ASE leading to nt-RNA, enabling: Differential expression analysis between cancer and normal tissue and across cancer types. Identification of different profiles of nt-RNA abundance and various factor which may be the causes of differential nt-RNA abundance and SiR results Identification of specific nt-RNA and toxic junctions that were expressed in various cancer (and/or normal tissue) types. Assessment of nt-RNA and their toxic junction expression as biomarkers or prognosis indicators. ResultsWe profiled the expressed known nt-RNA (toxic) junctions of known transcripts and discovered [~]22,000 novel toxic junctions out of [~]250,000 novel junctions found in the transcriptome data. The expression of nt-RNA was as high as 10% of all transcripts of the corresponding gene in cancer transcriptomes. Interestingly, some signature toxic junctions of nt-RNA are expressed in even higher quantities, e.g. up to 50% or more, which is reminiscent of a heterozygous mutation. We identified distinct patterns between cancer and normal samples, including example of nt-RNA expressing toxic junctions exclusively in normal or tumor samples. Clinically relevant examples included ANXA6 in breast cancer, where the nt-RNA isoform showed significantly higher expression in tumors (p=1.8e-15). In kidney renal clear cell carcinoma (KIRC), a significant isoform switch of ESYT2 based on the RNA-seq data was confirmed. The Kaplan-Meier survival curves showed that samples with the higher expression ratio of ESYT2-L are associated with better survival (p=2.0e-06). Unsupervised clustering showed that SiR results of 150 toxic signatures defined 4 subgroups of patients with different prognosis. Through principal component analysis (PCA), PC1 and PC2 can be used as an independent prognosis biomarkers. nt-RNA accounting for these PCs included splicing factors SRSF3 and CLK1, where CLK1 phosphorylates SRSF3 to promote exon 4 inclusion in both genes. ConclusionsIn summary, the expression profiles of all known and novel toxic junctions were explored using pan-cancer RNA-seq data. A dual 10% rule emerged from this study: [~]10% of novel junctions were toxic junctions associated with nt-RNA, and up to 10% of RNA transcripts inside a cell were also nt-RNA. The SiR metric enables accurate quantification of unproductive splicing and identification of cancer biomarkers. Our findings reveal that unproductive splicing represents functionally important post-transcriptional regulation in cancer. These expression profiles allow researchers to study the expression of nt-RNA signature junctions or novel signature junctions in or near the genes they are interested in, which could provide a new direction for their research. The SRSF3-CLK1 regulatory mechanism provides insights into splicing dysregulation. Our comprehensive toxic junction catalogue serves as a valuable resource, suggesting that targeting unproductive splicing pathways may offer novel therapeutic strategies for cancer treatment. Data availabilityThe catalogue is available on GitHub and UCSC browser. https://github.com/danhuang0909/nt_database for GitHub overview https://genome.ucsc.edu/s/dandan_0909/hg38_all_new_nr for genome browsing of all novel (unannotated) toxic junctions https://genome.ucsc.edu/s/dandan_0909/hg38_5_26 for toxic junctions in known (annotated) nt-RNA.

cancer biology↗

bioRxiv2025

Monocyte single cell-type gene expression measured in peripheral blood by DIRECT LS-TA method: the ratio-based biomarkers of (IFI27/PSAP) showed superior performance than interferon score in triage patients with viral infection

A rapid method to triage febrile patients into different categories of etiologies remains a significant challenge even nowadays, when many molecular tests for pathogens are available. Routine serum protein tests like C-reactive protein and procalcitonin have limited specificity. Host response gene signatures are promising biomarkers but they usually require assaying many genes, e.g. 7 genes are commonly used to calculate the interferon (IFN) score. However, these gene panels fail to capture cell-type-specific host responses. Measuring gene expression of a specified single cell population, like monocytes, offers enhanced biological insight. However, it currently requires laborious cell sorting or costly single-cell sequencing techniques, limiting its clinical applicability. This study aims to develop a simple ratio-based biomarker (RBB) representing monocyte-specific host response to viral infection called DIRECT LS-TA method. A simple ratio of 2 genes (both are shortlist monocyte informative genes) quantified in peripheral blood (PB) samples correlated with gene expression in purified monocytes in the corresponding individual. These RBBs cover 3 interferon-stimulated genes (ISGs): IFI27/PSAP, IFI44L/PSAP and SIGLEC1/PSAP. They are compared to the conventional multi-gene IFN score in the differentiation of viral infection. Public gene expression datasets from NCBI GEO were used to shortlist monocyte-informative genes that can be used as the RBB in PB. The DIRECT LS-TA RBB was calculated as the ratio of the target ISG transcript abundance (TA) to that of another reference gene (PSAP or CTSS) directly quantified from bulk PB data (e.g., Log(IFI27/PSAP) in WB). The correlation (expressed by coefficient of determination, R{superscript 2}) between these DIRECT LS-TA RBBs and the gold-standard target gene TA measured in purified monocytes was assessed. The diagnostic performance of selected RBBs (IFI27/PSAP, IFI44L/PSAP, SIGLEC1/PSAP) was compared against the conventional 8-gene IFN score for differentiating viral infections from controls. Direct LS-TA RBBs measured in PB showed strong correlation with gold-standard gene expression measured in purified monocytes (R2 ranged from 0.53 for the target gene IFI27 to >0.9 for the target gene IFI44L). This high level of correlation supports that this simple RBB (DIRECT LS-TA) method can replace the tedious cell sorting approach to obtain single-cell-type gene expression data. All DIRECT LS-TA results of ISGs were raised during viral infection. The best clinical performance in triaging viral infection patients was achieved by IFI27/PSAP or IFI27/CTSS across all datasets. For example, in the GSE111368 dataset, IFI27/PSAP achieved an AUC of 0.94 (95% CI 0.90-0.97) with 88% sensitivity and 95% specificity, surpassing the IFN scores AUC of 0.90 (95% CI 0.85-0.94) with 79% sensitivity and 93% specificity. ConclusionThe DIRECT LS-TA method, utilizing the format of simple two-gene ratio-based biomarkers like IFI27/PSAP, provides a robust and accurate measure of monocyte-specific interferon pathway activation directly from peripheral blood. The superior performance of the DIRECT LS-TA method makes it a promising, readily implementable tool for clinical triage. Its ability to provide single-cell-type specific information, rapid turnaround using standard qPCR/dPCR technology, and enhanced biological specificity make it a valuable molecular host response assessment.

immunology↗

bioRxiv2025

'Trans-differentiation of neutrophils from plasmablast' is an artefact caused by over-reliance on machine algorithms in single cell RNA sequencing analysis: Lesson learnt and steps ahead

Single cell RNA sequencing (scRNA-seq) provides new opportunities to characterize gene expression for individual cells. However, the sparse nature of the scRNA-seq data with many zero counts or missing values presents a major challenge to its analysis. The presence of low-quality cells further complicates the analysis. Here, we showed that the trans-differentiation of plasmablasts (activated plasma cells) into neutrophils reported in COVID patients (Wilk et al., 2020 in Nature Medicine) was an artefact of trajectory analysis. It was caused by {bsim}30 low-quality cells linking the 2 cell populations that are of unrelated lineages in hematopoietic differentiation. Such artefacts are not readily spotted during the current practice of peer reviews as the current statistical guidelines of most journals are not catered for big data such as that of scRNA-seq. New standards of statistics and quality control measures for machine algorithms are not in place and they are urgently needed to safeguard against over-interpretation of high dimensional data. We propose a comprehensive framework to ensure reproducibility in high-dimensional data analysis, emphasizing quality checks, sensitivity analyses, alternative and multiple algorithms validation. Finally, and most importantly, a hypothesis-driven research approach should be upheld.

bioinformatics↗

bioRxiv2025

Direct single cell-type gene expression analysis in Whole blood: Novel ratio-based gene expression biomarkers using 2 novel monocyte reference genes (PSAP and CTSS) for monitoring bacterial infection

BackgroundTo determine single cell-type specific gene expression in peripheral blood (PB) requires either prior labour-intense cell sorting or expensive single-cell RNA sequencing. We developed and validated a novel ratio-based biomarker (RBB) called Direct Leukocyte Subpopulation-Transcript Abundance (DIRECT LS-TA) assay that allows quantification of monocyte-specific gene expression directly from peripheral blood samples without cell sorting. MethodsThe DIRECT LS-TA method leverages known cell-type proportions and differential gene expression profiles among leukocyte subpopulations (e.g. monocytes, lymphocytes and granulocytes) to identify monocyte-informative genes. We shortlisted genes that had 2.5-fold higher expression in isolated monocytes compared to PB samples, indicating >50% transcript of genes in PB are contributed by monocytes alone. Public gene expression datasets were used to generate a list of monocyte informative genes with which DIRECT LS-TA assay is applicable in PB samples. PSAP and CTSS were identified as the monocyte informative reference genes, based on low biological variation (CV <12%) and high monocyte specificity. They were used as the denominator and together with another monocyte informative target gene as the numerator, the value of this new DIRECT LS-TA can be determined which is a kind of ratio-based biomarker (RBB). The clinical utility was the differentiation of patients with bacterial infection from control subjects in a discovery dataset and 4 other replication datasets. Methods to convert DIRECT LS-TA results to multiple of control median (MoM) provided approximations to delta-delta CT values of relative quantification commonly used in qPCR or digital PCR assays. ResultsOver 50 monocyte-informative genes were identified, including key immune response genes like IFI44L, IL1B, VNN1 and NFKBIZ. DIRECT LS-TA results showed excellent correlation with the gold standard results, gene expression in isolated monocyte expression (R2=0.55-0.97). Then, DIRECT LS-TA of these 50 target genes were evaluated to identify the best RBB to detect the host response to bacterial infection. Monocyte DIRECT LS-TA VNN1 RBB showed consistent upregulation across five independent datasets (median fold change 2.7-fold, p<10-8) with strong diagnostic performance (AUC=0.84-0.99). The expected corresponding delta-delta CT value is more than 1.4 cycle which can be reliably measured by qPCR. Additional monocyte-informative genes including NLRC4, CYP1B1, PFKFB3, LILRA5, NFKBIA, and NFKBIZ also demonstrated significant diagnostic ability (AUC>0.8). ConclusionsThe DIRECT LS-TA method (as a simple RBB) provides reliable quantification of monocyte-specific gene expression directly from whole blood samples without cell separation. The robust performance in bacterial infection diagnosis demonstrates its potential clinical utility for rapid infection differentiation and informed antibiotic stewardship. DIRECT LS-TA will emerge as a new kind of in vitro diagnostics (IVD) which can convey single cell-type gene expression information from PB samples. The new kind of IVD and uniqueness of the information, together with the ease of implementation will make it very useful in clinics

immunology↗

bioRxiv2024

Low-Rank Full Matrix Factorization for dropout imputation in single cell RNA-seq and benchmarking with imputation algorithms for downstream applications

BackgroundWhile single cell RNA sequencing becomes a powerful technology, the presence of the large number of zero counts represents a challenge for both wet-lab processing and data analysis. Imputation of these dropouts can now be performed by three categories of algorithms: Model or smoothing, Matrix theory or Deep learning. However, two fundamental questions remain unsettled: (1) whether imputation should be performed; (2) which imputation algorithm to use with various downstream applications. Notably, imputation is not commonly used in real scRNA-seq applications because of their uncertain benefits, concerns about false inferences in downstream applications, and the lack of in-depth benchmark. MethodsHere, we performed two tasks. First, we developed an algorithm using adaptive low-rank full matrix factorization (afMF) based on a previous limited implementation confined to using low rank matrix decomposition (ALRA). Second, to evaluate the impact of various imputation algorithms on downstream analyses, a new benchmark framework incorporating commonly used downstream applications was developed. This benchmark framework put emphasis on real datasets which had ground truth or matched bulk data such that algorithm performance was compared to more convinced data rather than less realistic simulated parameters. ResultsOur results indicated that afMF and ALRA (matrix based) provided good imputation and outperformed raw log-normalization in various downstream applications. afMF outperformed ALRA in several evaluations (cell-level differential expression analysis, GSEA, classification, biomarker prediction, clustering, SC-bulk profiling similarity). Besides, afMF ranked among the top levels in automatic cell type annotation, trajectory inference by DPT, and AUCell & SCENIC. Both showed acceptable scalability, while afMF had longer running time. MAGIC (smoothing based) and AutoClass (deep learning based) also performed well but may produce false positives. In contrast, more complicated methods (other deep learning or model based) were prone to overfitting and data distortion. We also found that certain downstream algorithms are not compatible with imputation, including trajectory inference with Slingshot and cell-cell communication. Prior imputation either showed no improvement or generated false positive findings with these downstream applications. ConclusionsWe hope this in-depth evaluation and the algorithm developed in this study can enhance the selection of appropriate imputation algorithm for specific scRNA-seq downstream analyses. The algorithm and the benchmark framework are available at GitHub: https://github.com/GO3295/SCImputation

bioinformatics↗

bioRxiv2019

Reference intervals of spot urine copper excretion in preschool children and potential application in pre-symptomatic screening of Wilson’s disease

BackgroundWith spot urine collected from a large control sample of preschool children (aged 3-7 years), reference range of spot urine copper excretion indexes and their biological variation were defined.\n\nMethodsIn order to investigate their test performance in screening of Wilson disease in this age group, multiple spot urine samples from 6 WD patients diagnosed at presymptomatic stage were analysed. Cut-off values for spot urine copper concentration, copper to creatinine ratio and copper to osmolality ratio at 0.5 {micro}mol/L, 0.1 {micro}mol/mmol and 0.00085 {micro}mol/mOsmol (32 {micro}g/L, 56 {micro}g/g creatinine and 0.054 {micro}g/mOsmol, respectively, in conventional units) have potential application in differentiation of WD patients.\n\nResultsThe data provides a new insight that the inter-individual variation of spot urine copper indexes (CVg) were moderate with figures around 60% which was similar to other clinically useful urine tests, such as urine albumin excretion ratio. Spot urine copper excretion strongly correlated with both urine creatinine and osmolality. And more than 95% of data points in health preschool children fell within prediction regions by linear regression suggesting a good utility of normalisation by these 2 analytes. Receiver operator curve (ROC) showed that copper to osmolality ratio was the best index with an area under curve (AUC) greater than 0.98.\n\nConclusionsBased on the data, a new WD screening time window targeting preschool children is proposed. Application of a bivariate screening strategy using spot urine copper concentration and urine osmolality may be useful in a population screening program for preschool children.

pathology↗