Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE.

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

Expanding Extender Substrate Selection for Unnatural Polyketide Biosynthesis by Acyltransferase Domain Exchange within a Modular Polyketide Synthase.

Modular polyketide synthases (PKSs) are polymerases that employ α-carboxyacyl-CoAs as extender substrates. This enzyme family contains several catalytic modules, where each module is responsible for a single round of polyketide chain extension. Although PKS modules typically use malonyl-CoA or methylmalonyl-CoA for chain elongation, many other malonyl-CoA analogues are used to diversify polyketide structures in nature. Previously, we developed a method to alter an extension substrate of a given module by exchanging an acyltransferase (AT) domain while maintaining protein folding. Here, we report in vitro polyketide biosynthesis by 13 PKSs (the wild-type PKS and 12 AT-exchanged PKSs with unusual ATs) and 14 extender substrates. Our ∼200 in vitro reactions resulted in 13 structurally different polyketides, including several polyketides that have not been reported. In some cases, AT-exchanged PKSs produced target polyketides by >100-fold compared to the wild-type PKS. These data also indicate that most unusual AT domains do not incorporate malonyl-CoA and methylmalonyl-CoA but incorporate various rare extender substrates that are equal to in size or slightly larger than natural substrates. We developed a computational workflow to predict the approximate AT substrate range based on active site volumes to support the selection of ATs. These results greatly enhance our understanding of rare AT domains and demonstrate the benefit of using the proposed PKS engineering strategy to produce novel chemicals in vitro.

Investigating higher-order interactions in single-cell data with scHOT.

Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered 'pseudotime' offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT-single-cell higher-order testing-which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene-gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.

scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction.

MOTIVATION: With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into samples from individuals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each sample (e.g. individuals). RESULTS: Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. We demonstrate that summarizing a broad collection of features at the sample level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of individuals. AVAILABILITY AND IMPLEMENTATION: scFeatures is publicly available as an R package at https://github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Section 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Scalable workflow for characterization of cell-cell communication in COVID-19 patients.

COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use this to examine eight public single-cell RNA-seq datasets (six from peripheral blood mononuclear cells, one from bronchoalveolar lavage and one from nasopharyngeal), with a total of 211 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions.

Survival and clinicopathological significance of blood vessel invasion in operable breast cancer: a systematic review and meta-analysis.

BACKGROUND: Lymphovascular invasion, including lymphatic-vessel invasion and blood-vessel invasion, plays an important role in distant metastases. The metastatic pattern of blood-vessel invasion may differ from that of lymphatic-vessel invasion. However, its prognostic significance in breast cancer remains controversial. We evaluated the role of blood-vessel invasion in the prognosis of operable breast-cancer patients and its association with clinicopathological characteristics. METHODS: We systematically searched EMBASE, PubMed, the Cochrane Library and Web of Science for studies in English through December 2020. Disease-free survival, overall survival and cancer-specific survival were the primary outcomes. Pooled hazard ratios and 95% confidence intervals were assessed using a random-effects model. RESULTS: Twenty-seven studies involving 7954 patients were included. Blood-vessel invasion occurred in 20.4% of tumor samples. Pooled results showed significant associations of blood-vessel invasion with worse disease-free survival (hazard ratio = 1.82; 95% confidence interval = 1.43-2.31) and overall survival (hazard ratio = 1.86; 95% confidence interval = 1.16-2.99) in multivariate analyses. The results of the univariate analyses were similar. Among the clinicopathological factors, blood-vessel invasion was associated with larger tumor size, lymph-node metastasis, nonspecific invasive type, higher histological grade, estrogen receptor-negative breast cancer, human epidermal growth factor receptor 2-positive breast cancer and lymphatic-vessel invasion. In the lymph-node-negative subgroup analyses, the presence of blood-vessel invasion led to poorer disease-free survival (hazard ratio = 2.46; 95%confidence interval = 1.64-3.70) and overall survival (hazard ratio = 2.94; 95%confidence interval = 1.80-4.80). CONCLUSIONS: We concluded that blood-vessel invasion is an independent predictor of poor prognosis in operable breast cancer and is associated with aggressive clinicopathological features. Breast-cancer patients with blood-vessel invasion require more aggressive treatments after surgery.

Case report of Salmonella derby septicemia complicated with co-occurrence of disseminated intravascular coagulation and thrombotic microangiopathy.

BACKGROUND: Both disseminated intravascular coagulation and thrombotic microangiopathy are complications of sepsis as Salmonella septicemia, respectively. They are related and have similar clinical characteristics as thrombopenia and organ dysfunctions. They rarely co-occur in some specific cases, which requires a clear distinction. CASE PRESENTATION: A 22-year-old woman had just undergone intracranial surgery and suffered from Salmonella derby septicemia with multiorgan involvement in the hospital. Laboratory workup demonstrated coagulation disorder, hemolytic anemia, thrombocytopenia, and acute kidney injury, leading to the co-occurrence of disseminated intravascular coagulation and secondary thrombotic microangiopathy. She received antibiotics, plasma exchange therapy, dialysis, mechanical ventilation, fluids, and vasopressors and gained full recovery without complications. CONCLUSION: Disseminated intravascular coagulation and secondary thrombotic microangiopathy can co-occur in Salmonella derby septicemia. They should be treated cautiously in diagnosis and differential diagnosis. Thrombotic microangiopathy should not be missed just because of the diagnosis of disseminated intravascular coagulation. Proper and timely identification of thrombotic microangiopathy with a diagnostic algorithm is essential for appropriate treatment and better outcomes.

scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets.

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

Toward High-Performance Electrochromic Conjugated Polymers: Influence of Local Chemical Environment and Side-Chain Engineering.

Three homologous electrochromic conjugated polymers, each containing an asymmetric building block but decorated with distinct alkyl chains, were designed and synthesized using electrochemical polymerization in this study. The corresponding monomers, namely T610FBTT810, DT6FBT, and DT48FBT, comprise the same backbone structure, i.e., an asymmetric 5-fluorobenzo[c][1,2,5]thiadiazole unit substituted by two thiophene terminals, but were decorated with different types of alkyl chain (hexyl, 2-butyloctyl, 2-hexyldecyl, or 2-octyldecyl). The effects of the side-chain structure and asymmetric repeating unit on the optical absorption, electrochemistry, morphology, and electrochromic properties were investigated comparatively. It was found that the electrochromism conjugated polymer, originating from DT6FBT with the shortest and linear alkyl chain, exhibits the best electrochromic performance with a 25% optical contrast ratio and a 0.3 s response time. The flexible electrochromic device of PDT6FBT achieved reversible colors of navy and cyan between the neutral and oxidized states, consistent with the non-device phenomenon. These results demonstrate that subtle modification of the side chain is able to change the electrochromic properties of conjugated polymers.

Impact of similarity metrics on single-cell RNA-seq data clustering.

Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on individual cells from complex samples. A common goal in scRNA-seq data analysis is to discover and characterise cell types, typically through clustering methods. The quality of the clustering therefore plays a critical role in biological discovery. While numerous clustering algorithms have been proposed for scRNA-seq data, fundamentally they all rely on a similarity metric for categorising individual cells. Although several studies have compared the performance of various clustering algorithms for scRNA-seq data, currently there is no benchmark of different similarity metrics and their influence on scRNA-seq data clustering. Here, we compared a panel of similarity metrics on clustering a collection of annotated scRNA-seq datasets. Within each dataset, a stratified subsampling procedure was applied and an array of evaluation measures was employed to assess the similarity metrics. This produced a highly reliable and reproducible consensus on their performance assessment. Overall, we found that correlation-based metrics (e.g. Pearson's correlation) outperformed distance-based metrics (e.g. Euclidean distance). To test if the use of correlation-based metrics can benefit the recently published clustering techniques for scRNA-seq data, we modified a state-of-the-art kernel-based clustering algorithm (SIMLR) using Pearson's correlation as a similarity measure and found significant performance improvement over Euclidean distance on scRNA-seq data clustering. These findings demonstrate the importance of similarity metrics in clustering scRNA-seq data and highlight Pearson's correlation as a favourable choice. Further comparison on different scRNA-seq library preparation protocols suggests that they may also affect clustering performance. Finally, the benchmarking framework is available at http://www.maths.usyd.edu.au/u/SMS/bioinformatics/software.html.

CiteFuse enables multi-modal analysis of CITE-seq data.

MOTIVATION: Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and cell-surface proteins. Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand-receptor interaction analysis and interactive web-based visualization of CITE-seq data. RESULTS: We demonstrate the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single-modality profiling using both simulations and real-world CITE-seq data. Furthermore, we illustrate a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Finally, we demonstrate CiteFuse for predicting ligand-receptor interactions by using multi-modal CITE-seq data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data. AVAILABILITY AND IMPLEMENTATION: CiteFuse is freely available at http://shiny.maths.usyd.edu.au/CiteFuse/ as an online web service and at https://github.com/SydneyBioX/CiteFuse/ as an R package. CONTACT: pengyi.yang@sydney.edu.au. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

scClassify: sample size estimation and multiscale classification of cells using single and multiple reference.

Automated cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalise on the large collection of well-annotated scRNA-seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single-cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state-of-the-art methodology in automated cell type identification from scRNA-seq data.

The prediction value of Glasgow coma scale-pupils score in neurocritical patients: a retrospective study.

BACKGROUND: External validation is necessary before  its clinical recommendation in new setting. The aim is to externally validate Glasgow Coma Scale-pupils score (GCS-P) in neurocritical patients and to compare its performances with Glasgow Coma Scale (GCS) and its derivatives. METHODS: GCS-P at admission was calculated for individual based on the model developed by Brennan et al. Area under the receiver operating characteristic curves (AUCs), Nagelkerke's R2 and Brier scores were used to assess external validity of GCS-P to predict mortality in neurocritical patients and to compare predictive performance with GCS and its derivatives. SUBJECTS: 4372 neurocritical patients from intensive care units of Beth Israel Deaconess Medical Center, United States between 2001 and 2012. RESULTS: GCS-P showed good discrimination (AUC 0.847 for in-hospital mortality and 0.774 for ninety-day mortality), modest calibration (Nagelkerke's R2 33.1% for in-hospital mortality and 23.3% for ninety-day mortality). Predictive performances of GCS and its derivatives was inferior to GCS-P. CONCLUSIONS: GCP-P discriminated well in between death in neurocritical patients. GCP-P improved predictive performance for short-term mortality over GCS and its derivatives in neurocritical patients. It would be a simple, early and reasonable daily routine option for prognosis assessment in neurocritical setting.

Mutational and transcriptomic landscapes of a rare human prostate basal cell carcinoma.

BACKGROUND: As a rare subtype of prostate carcinoma, basal cell carcinoma (BCC) has not been studied extensively and thus lacks systematic molecular characterization. METHODS: Here, we applied single-cell genomic amplification and RNA-Seq to a specimen of human prostate BCC (CK34ßE12+ /P63+ /PAP- /PSA- ). The mutational landscape was obtained via whole exome sequencing of the amplification mixture of 49 single cells, and the transcriptomes of 69 single cells were also obtained. RESULTS: The five putative driver genes mutated in BCC are CASC5, NUTM1, PTPRC, KMT2C, and TBX3, and the top three nucleotide substitutions are C>T, T>C, and C>A, similar to common prostate cancer. The distribution of the variant allele frequency values indicated that these single cells are from the same tumor clone. The 69 single cells were clustered into tumor, stromal, and immune cells based on their global transcriptomic profiles. The tumor cells specifically express basal cell markers like KRT5, KRT14, and KRT23 and epithelial markers EPCAM, CDH1, and CD24. The transcription factor covariance network analysis showed that the BCC tumor cells have distinct regulatory networks. By comparison with current prostate cancer datasets, we found that some of the bulk samples exhibit basal cell signatures. Interestingly, at single-cell resolution the gene expression patterns of prostate BCC tumor cells show uniqueness compared with that of common prostate cancer-derived circulating tumor cells. CONCLUSIONS: This study, for the first time, discloses the comprehensive mutational and transcriptomic landscapes of prostate BCC, which lays a foundation for the understanding of its tumorigenesis mechanism and provides new insights into prostate cancers in general.

scDC: single cell differential composition analysis.

BACKGROUND: Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis - none of the existing methods can identify cell-type perfectly and variability related to cell sampling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition. RESULTS: We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap resampling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions. CONCLUSIONS: scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap resampling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of the scdney package (Single Cell Data Integrative Analysis) R package, available from https://github.com/SydneyBioX/scdney.

Severe Jarisch-Herxheimer Reaction (JHR) in a leptospirosis patient: A case report.

Leptospirosis is a zoonosis that is related to potential respiratory, renal, neurological, and cardiovascular failure. At present, antibiotics are the recommended treatment, but due to the underlying cause of the disease, they may induce the Jarisch-Herxheimer reaction (JHR) within 24 hours. At the same time, we speculate that JHR may aggravate the natural course of leptospirosis. Considering that there are few available reports on this event, we will share a case of pulmonary hemorrhagic leptospirosis, where antibiotic treatment is suspected to have triggered the JHR. This report is expected to improve clinical attention to the relationship between leptospirosis and JHR.

SANTO: a coarse-to-fine alignment and stitching method for spatial omics.

With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.

Prediction of acute kidney injury after cardiac surgery with fibrinogen-to-albumin ratio: a prospective observational study.

Background: The occurrence of acute kidney injury (AKI) following cardiac surgery is common and linked to unfavorable consequences while identifying it in its early stages remains a challenge. The aim of this research was to examine whether the fibrinogen-to-albumin ratio (FAR), an innovative inflammation-related risk indicator, has the ability to predict the development of AKI in individuals after cardiac surgery. Methods: Patients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. To assess the diagnostic value of the FAR in predicting AKI, calculations were performed for the area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Of the 260 enrolled patients, 85 developed AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI), 1.064-1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR on admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616-0.754]. Although the AUC-ROC of the prediction model was not substantially improved by adding FAR, continuous NRI and IDI were significantly improved. Conclusions: FAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.

BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.