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In recent years, the role of intrinsic biophysical features, especially cellular stiffness, in diverse cellular and disease processes is being increasingly recognized. New high throughput techniques for the quantification of cellular stiffness facilitate the study of their roles in health and diseases. In this review, we summarized recent discovery about how cellular stiffness is involved in cell stemness, tumorigenesis, and blood diseases. In addition, we review the molecular mechanisms underlying the gene regulation of cellular stiffness in health and disease progression. Finally, we discussed the current understanding on how the cytoskeleton structure and the regulation of these genes contribute to cellular stiffness, highlighting where the field of cellular stiffness is headed.

The rapidly growing collection of public single-cell sequencing data have become a valuable resource for molecular, cellular and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our scalability benchmarking experiments, Vulture can outperform the state-of-the-art cloud-based pipeline Cumulus with a 40% and 80% reduction of runtime and cost, respectively. Furthermore, Vulture is 2-10 times faster than PathogenTrack and Venus, while generating comparable results. We applied Vulture to two COVID-19, three hepatocellular carcinoma (HCC), and two gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell-type specific enrichment of SARS-CoV2, hepatitis B virus (HBV), andH. pyloripositive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license athttps://github.com/holab-hku/Vulture.

PD-L1 (programmed death-ligand 1) serves as a pivotal immune checkpoint in both the innate and adaptive immune systems. PD-L1 is expressed in macrophages in response to interferon-gamma (IFNγ). We examined whether PD-L1 might regulate macrophage development. We establishedPD-L1-/-human pluripotent stem cells, differentiated them into macrophages, and observed a 60% reduction of CD11B+CD45+macrophages inPD-L1-/-, orthogonally verified with PD-L1 inhibitor BMS-1166 reduced macrophages to the same fold. Single-cell RNA sequencing further confirmed the 60% reduction of macrophages as well as the down-regulation of macrophage-defining transcription factorsSPI1, KLF6, andMAFB. Further,PD-L1-/-macrophages reduced the level of inflammatory signals such as NFκB, TNF, and chemokines CXCL and CCL families. Whilst anti-inflammatory TGF-β was upregulated. Finally, we identified thatPD-L1-/-macrophages significantly down-regulated interferon-stimulated genes (ISGs) despite IFNγ in differentiation media. Mechanistically,PD-L1-/-macrophages reducedIFNGR1expression explaining that cells could not respond to IFNγ. These data suggest that PD-L1 regulates inflammatory macrophage development by maintaining the IFNγ signal.

Recent advancements in the use of single-cell technologies in large cohort studies enable the investigation of cellular response and mechanisms associated with disease outcome, including COVID-19. Several efforts have been made using single-cell RNA-sequencing to better understand the immune response to COVID-19 virus infection. Nonetheless, it is often difficult to compare or integrate data from multiple data sets due to challenges in data normalisation, metadata harmonisation, and having a common interface to quickly query and access this vast amount of data. Here we present Covidscope (http://covidsc.d24h.hk/), a well-curated open web resource that currently contains single-cell gene expression data and associated metadata of almost 5 million blood and immune cells extracted from almost 1,000 COVID-19 patients across 20 studies around the world. Our collection contains the integrated data with harmonised metadata and multi-level cell type annotations. By combining NoSQL and optimised index, our Covidscope achieves rapid subsetting of high-dimensional gene expression data based on both data set level, donor-level (e.g., age and sex of patients) and cell-level (e.g., expression of specific gene markers) metadata, enabling multiple efficient downstream single-cell meta-analysis.

SUMMARYThis study develops a versatile cell multiplexing and data analysis platform to gain knowledge gain into mechanisms of cell differentiation. We engineer a cell barcoding system in human cells enabling multiplexed single-cell RNA sequencing for high throughput perturbation of customisable and diverse experimental conditions. This is coupled with a new computational analysis pipeline that overcomes the limitations of conventional algorithms by using an unsupervised, genome-wide, orthogonal biological reference point to reveal the cell diversity and regulatory networks in the input scRNA-seq data set. We implement this pipeline by engineering transcribed barcodes into induced pluripotent stem cells and multiplex 62 independent experimental conditions comprising eight differentiation time points and nine developmental signalling perturbations in duplicates. We identify and deconstruct the temporal, signalling, and gene regulatory imperatives of iPSC differentiation into cell types of ectoderm, mesoderm, and endoderm lineages. This study provides a cellular and computational pipeline to study cell differentiation applicable to studies in developmental biology, drug discovery, and disease modelling.

Microfluidic paper-based analytical devices (μPADs) have been widely explored for point-of-care testing due to their simplicity, low cost, and portability. μPADs with multiple-step reactions usually require precise flow control, especially flow-delay. This paper reports the numerical, mathematical, and experimental studies of flow delay through wax valves surrounded by PDMS walls on paper microfluidics. The predried surfactant in the sample zone diffuses into the liquid sample which can therefore flow through the wax valves. The delay time is automatically regulated by the diffusion of the surfactant after sample loading. The numerical study suggested that both the elevated contact angle and the reduced porosity and pore size in the wax printed region could effectively prevent water but allow liquids with lower contact angles (e.g., surfactant solutions) to flow through. The PDMS walls fabricated using a low-cost liquid dispenser effectively prevented the leakage of surfactant solutions. By controlling the quantity, diffusion distance, and type of the surfactant predried on the chip, the system successfully achieved a delay time ranging from 1.6 to 20 minutes. A mathematical model involving the above parameters was developed based on Fick's second law to predict the delay time. Finally, the flow-delay systems were applied in sequential mixing and distance-based detection of either glucose or alcohol. Linear ranges of 1-100 mg dL-1 and 1-40 mg dL-1 were achieved for glucose and alcohol, respectively. The lower limit detection (LOD) of glucose and alcohol was 1 mg dL-1. The LOD of glucose was only 1/11 of that detected using μPADs without flow control, indicating the advantage of controlling fluid flow. The systematic findings in this study provide critical guidelines for the development and applications of wax valves in automatic flow delay for point-of-care testing. This journal is

Background Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). Materials and methods Patients were retrospectively enrolled between April 2007 and December 2018 from two institutions. We extracted deep learning features of six pre-trained convolutional neural networks, respectively, from pretreatment CT images in the training cohort (n = 161). Support vector machine was adopted as the classifier. Validation was performed in an external testing cohort (n = 70). We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model consisting of clinical factors only was also built for baseline comparison. We further conducted a radiogenomics analysis using gene expression profiles to reveal underlying biology associated with radiological prediction. Results The optimal model with features extracted from ResNet50 achieved an AUC and accuracy of 0.805 (95% CI, 0.696–0.913) and 77.1% (65.6%-86.3%) in the testing cohort, compared with 0.725 (0.605–0.846)) and 67.1% (54.9%-77.9%) for the radiomics model. All the radiological models showed better predictive performance than the clinical model. Radiogenomics analysis suggested a potential association mainly with WNT signaling pathway and tumor microenvironment. Conclusions The novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.

Cancer growth is usually accompanied by metastasis which kills most cancer patients. Here we aim to study the effect of cisplatin at different doses on breast cancer growth and metastasis. Methods: We used cisplatin to treat breast cancer cells, then detected the migration of cells and the changes of epithelial-mesenchymal transition (EMT) markers by migration assay, Western blot, and immunofluorescent staining. Next, we analyzed the changes of RNA expression of genes by RNA-seq and confirmed the binding of activating transcription factor 3 (ATF3) to cytoskeleton related genes by ChIP-seq. Thereafter, we combined cisplatin and paclitaxel in a neoadjuvant setting to treat xenograft mouse models. Furthermore, we analyzed the association of disease prognosis with cytoskeletal genes and ATF3 by clinical data analysis. Results: When administered at a higher dose (6 mg/kg), cisplatin inhibits both cancer growth and metastasis, yet with strong side effects, whereas a lower dose (2 mg/kg) cisplatin blocks cancer metastasis without obvious killing effects. Cisplatin inhibits cancer metastasis through blocking early steps of EMT. It antagonizes transforming growth factor beta (TGFβ) signaling through suppressing transcription of many genes involved in cytoskeleton reorganization and filopodia formation which occur early in EMT and are responsible for cancer metastasis. Mechanistically, TGFβ and fibronectin-1 (FN1) constitute a positive reciprocal regulation loop that is critical for activating TGFβ/SMAD3 signaling, which is repressed by cisplatin induced expression of ATF3. Furthermore, neoadjuvant administration of cisplatin at 2 mg/kg in conjunction with paclitaxel inhibits cancer growth and blocks metastasis without causing obvious side effects by inhibiting colonization of cancer cells in the target organs. Conclusion: Thus, cisplatin prevents breast cancer metastasis through blocking early EMT, and the combination of cisplatin and paclitaxel represents a promising therapy for killing breast cancer and blocking tumor metastasis.

It is my pleasure to write a few words to introduce myself to the readers of Biophysical Reviews as part of the ‘meet the editors’ series. [Image: see text] A portrait of Dr. Joshua Ho

Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, identifying informative mtDNA variants in noisy and sparse single-cell sequencing data is still challenging with few computation methods available. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA, DNA or ATAC sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrate its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution.

Short-term antibiotics exposure is associated with alterations in microbiota and antibiotic resistance genes (ARGs) in the human gut. While antibiotics are critical in the successful eradication of Helicobacter pylori, the short-term and long-term impacts on the composition and quantity of antibiotics resistance genes after H. pylori eradication are unclear. This study used whole-genome shotgun metagenomic of stool samples to characterize the gut microbiota and ARGs, before and after H. pylori eradication therapy.Forty-four H. pylori-infected patients were recruited, including 21 treatment naïve patients who received clarithromycin-based triple therapy (CLA group) and 23 patients who failed previous therapies, in which 10 received levofloxacin-based quadruple therapy (LEVO group) and 13 received other combinations (OTHER group). Stool samples were collected at baseline (before current treatment), 6 week and 6 month after eradication therapy. At baseline, there was only a slight difference among the three groups on ARGs and gut microbiota. After eradication therapy, there was a transient but significant increase in gut ARGs 6 week post-therapy, among which the LEVO group had the most significant ARGs alteration compared to other two groups. For treatment naïve patients, those with higher ErmF abundance were prone to fail CLA eradication and gain more ARGs after treatment. For gut microbiota, the bacteria richness decreased at 6 week and there was a significant difference in microbiota community among the three groups at 6 week.Our findings demonstrated the dynamic alterations in gut microbiota and ARGs induced by different eradication therapies, which could influence the choices of antibiotics in eradication therapy.

Mitochondrial energy production and function rely on optimal concentrations of the essential redox-active lipid, coenzyme Q (CoQ). CoQ deficiency results in mitochondrial dysfunction associated with increased mitochondrial oxidative stress and a range of pathologies. What drives CoQ deficiency in many of these pathologies is unknown, just as there currently is no effective therapeutic strategy to overcome CoQ deficiency in humans. To date, large-scale studies aimed at systematically interrogating endogenous systems that control CoQ biosynthesis and their potential utility to treat disease have not been carried out. Therefore, we developed a quantitative high-throughput method to determine CoQ concentrations in yeast cells. Applying this method to the Yeast Deletion Collection as a genome-wide screen, 30 genes not known previously to regulate cellular concentrations of CoQ were discovered. In combination with untargeted lipidomics and metabolomics, phosphatidylethanolamine N-methyltransferase (PEMT) deficiency was confirmed as a positive regulator of CoQ synthesis, the first identified to date. Mechanistically, PEMT deficiency alters mitochondrial concentrations of one-carbon metabolites, characterized by an increase in the S-adenosylmethionine to S-adenosylhomocysteine (SAM-to-SAH) ratio that reflects mitochondrial methylation capacity, drives CoQ synthesis, and is associated with a decrease in mitochondrial oxidative stress. The newly described regulatory pathway appears evolutionary conserved, as ablation of PEMT using antisense oligonucleotides increases mitochondrial CoQ in mouse-derived adipocytes that translates to improved glucose utilization by these cells, and protection of mice from high-fat diet-induced insulin resistance. Our studies reveal a previously unrecognized relationship between two spatially distinct lipid pathways with potential implications for the treatment of CoQ deficiencies, mitochondrial oxidative stress/dysfunction, and associated diseases., Graphical abstract Image 1, Highlights • Mitochondrial CoQ deficiency results in oxidative stress and a range of pathologies • The drivers of mitochondrial CoQ deficiency remain largely unknown • PEMT deficiency is the first identified positive regulator of mitochondrial CoQ • PEMT deficiency increases CoQ by increasing the mitochondrial SAM-to-SAH ratio • PEMT deficiency prevents insulin resistance by increasing mitochondrial CoQ

This cohort study assesses the incidence of emergency department (ED) visits in Hong Kong, China, for sexual abuse among youth before and during the COVID-19 pandemic.

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset., Scalable trajectory inference for multi-omic single cell datasets is challenging in terms of capturing non-tree complex topologies. Here the authors present a method, VIA, that scales to millions of cells across multiple omic modalities using lazy-teleporting random walks.

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9’s activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.5-fold in comparison to the null model. Using this approach and followed by structure-guided engineering, we identify the N888R/A889Q variant conferring increased editing activity on the protospacer adjacent motif-relaxed KKH variant of Cas9 nuclease from Staphylococcus aureus (KKH-SaCas9) and its derived base editor in human cells. Our work validates a readily applicable workflow to enable resource-efficient high-throughput engineering of genome editor’s activity.

Importance: Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clinical image analysis can be used for diagnosis and prognosis in cancers, including oral SCC; its use in these areas can improve patient care and outcome. Observations: This review is a summary of the use of deep learning models for diagnosis, prognosis, and metastasis detection for oral SCC by analyzing information from pathological and radiographic images. Specifically, deep learning has been used to classify different cell types, to differentiate cancer cells from nonmalignant cells, and to identify oral SCC from other cancer types. It can also be used to predict survival, to differentiate between tumor grades, and to detect lymph node metastasis. In general, the performance of these deep learning models has an accuracy ranging from 77.89% to 97.51% and 76% to 94.2% with the use of pathological and radiographic images, respectively. The review also discusses the importance of using good-quality clinical images in sufficient quantity on model performance. Conclusions: and Relevance Applying pathological and radiographic images in deep learning models for diagnosis and prognosis of oral SCC has been explored, and most studies report results showing good classification accuracy. The successful use of deep learning in these areas has a high clinical translatability in the improvement of patient care.

Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9–95.8%; Sensitivity: 55.5–77.8%; Specificity: 91.5–98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

Background Read alignment and transcript assembly are the core of RNA-seq analysis for transcript isoform discovery. Nonetheless, current tools are not designed to be scalable for analysis of full-length bulk or single cell RNA-seq (scRNA-seq) data. The previous version of our cloud-based tool Falco only focuses on RNA-seq read counting, but does not allow for more flexible steps such as alignment and read assembly. Results The Falco framework can harness the parallel and distributed computing environment in modern cloud platforms to accelerate read alignment and transcript assembly of full-length bulk RNA-seq and scRNA-seq data. There are two new modes in Falco: alignment-only and transcript assembly. In the alignment-only mode, Falco can speed up the alignment process by 2.5–16.4x based on two public scRNA-seq datasets when compared to alignment on a highly optimised standalone computer. Furthermore, it also provides a 10x average speed-up compared to alignment using published cloud-enabled tool for read alignment, Rail-RNA. In the transcript assembly mode, Falco can speed up the transcript assembly process by 1.7–16.5x compared to performing transcript assembly on a highly optimised computer. Conclusion Falco is a significantly updated open source big data processing framework that enables scalable and accelerated alignment and assembly of full-length scRNA-seq data on the cloud. The source code can be found at https://github.com/VCCRI/Falco.

DamID, in which a protein of interest is fused to Dam methylase, enables mapping of protein-DNA binding through readout of adenine methylation in genomic DNA. DamID offers a compelling alternative to chromatin immunoprecipitation sequencing (ChIP-Seq), particularly in cases where cell number or antibody availability is limiting. This comes at a cost, however, of high non-specific signal and a lowered spatial resolution of several kb, limiting its application to transcription factor-DNA binding. Here we show that mutations in Dam, when fused to the transcription factor Tcf7l2, greatly reduce non-specific methylation. Combined with a simplified DamID sequencing protocol, we find that these Dam mutants allow for accurate detection of transcription factor binding at a sensitivity and spatial resolution closely matching that seen in ChIP-seq. Electronic supplementary material The online version of this article (10.1186/s13072-019-0273-x) contains supplementary material, which is available to authorized users.

Congenital heart disease (CHD) affects up to 1% of live births. However, a genetic diagnosis is not made in most cases. The purpose of this study was to assess the outcomes of genome sequencing (GS) of a heterogeneous cohort of CHD patients. Ninety-seven families with probands born with CHD requiring surgical correction were recruited for genome sequencing. At minimum, a proband–parents trio was sequenced per family. GS data were analyzed via a two-tiered method: application of a high-confidence gene screen (hcCHD), and comprehensive analysis. Identified variants were assessed for pathogenicity using the American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG-AMP) guidelines. Clinically relevant genetic variants in known and emerging CHD genes were identified. The hcCHD screen identified a clinically actionable variant in 22% of families. Subsequent comprehensive analysis identified a clinically actionable variant in an additional 9% of families in genes with recent disease associations. Overall, this two-tiered approach provided a clinically relevant variant for 31% of families. Interrogating GS data using our two-tiered method allowed identification of variants with high clinical utility in a third of our heterogeneous cohort. However, association of emerging genes with CHD etiology, and development of novel technologies for variant assessment and interpretation, will increase diagnostic yield during future reassessment of our GS data.

SummaryTargeted therapeutics have advanced cancer treatment, but single agent activity remains limited by de novo and acquired resistance. Combining targeted drugs is broadly seen as a way to improve treatment outcome, motivating the ongoing search for efficacious combinations. To identify synergistic targeted therapy combinations and study the impact of tumor heterogeneity on combination outcome, we systematically tested over 5,000 two drug combinations at multiple doses across a collection of 81 non-small cancer cell lines. Both known and novel synergistic combinations were identified. Strikingly, very few combinations yield synergy across the majority of cell line models. Importantly, synergism mainly arises due to sensitization of single agent resistant models, rather than further sensitize already sensitive cell lines, frequently via dual targeting of a single or two highly interconnected pathways. This drug combinations resource, the largest of its kind should help delineate new synergistic regimens by facilitating the understanding of drug synergism in cancer.

Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

The cell elasticity has attracted extensive research interests since it not only provides new insights into cell biology but also is an emerging mechanical marker for the diagnosis of some diseases. This paper reports the method for the precise measurement of mechanical properties of single cells deformed to a large extent using a novel microfluidic system integrated with a pressure feedback system and small particle separation unit. The particle separation system was employed to avoid the blockage of the cell deformation channel to enhance the measurement throughput. This system is of remarkable application potential in the precise evaluation of cell mechanical properties. In brief, this paper reports:•The manufacturing of the chip using standard soft lithography;•The methods to deform single cells in a microchannel and measure the relevant pressure drop using a pressure sensor connecting to the microfluidic chip;•Calculation of the mechanical properties including stiffness and fluidity of each cell based on a power-law rheology model describing the viscoelastic behaviors of cells;•Automatic and real-time measurement of the mechanical properties using video processing software., Graphical abstract Image, graphical abstract

Motivation Scalable clustering algorithms are needed to analyze millions of cells in single cell RNA-seq (scRNA-seq) data. Results Here, we present an open source python package called FlowGrid that can integrate into the Scanpy workflow to perform clustering on very large scRNA-seq datasets. FlowGrid implements a fast density-based clustering algorithm originally designed for flow cytometry data analysis. We introduce a new automated parameter tuning procedure, and show that FlowGrid can achieve comparable clustering accuracy as state-of-the-art clustering algorithms but at a substantially reduced run time for very large single cell RNA-seq datasets. For example, FlowGrid can complete a one-hour clustering task for one million cells in about five min. Availability and implementation https://github.com/holab-hku/FlowGrid. Supplementary information Supplementary data are available at Bioinformatics online.

MotivationAdvances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of individual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful.ResultsWe present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms.Availability and implementationstarmapVR is freely accessible athttps://holab-hku.github.io/starmapVR, with the corresponding source code available athttps://github.com/holab-hku/starmapVRunder the open source MIT license.Supplementary InformationSupplementary data are available atBioinformaticsonline.

With advancements in popular modern wearable devices, such as Apple Watch and Fitbit, it is now possible to harness these technologies for continuous monitoring and recording of heart rate data, which can then be used for medical research and ultimately e-health applications. In this paper, we report the development of a new mobile smartphone application (app) that enables heart rate profiles to be extracted and analysed from continuous heart rate monitoring time series. The new iOS app, called Hactive, extracts heart rate data from Apple’s smartwatches to construct heart rate profiles. A key innovation is Hactive’s ability to detect and analyse exercise-associated heart rate changes from continuous heart rate data, which enables heart rate profiles to be constructed based on free-living conditions. We believe this tool advances the use of wearable technology to collect physiologically relevant big data for healthcare and medical research. The source code of Hactive is available via an MIT open source licence at https://github.com/VCCRI/hactive .

Predicting where transcription factors bind in the genome from their in vitro DNA-binding affinity is confounded by the large number of possible interactions with nearby transcription factors. To characterize the in vivo binding logic for the Wnt effector Tcf7l2, we developed a high-throughput screening platform in which thousands of synthesized DNA phrases are inserted into a specific genomic locus, followed by measurement of Tcf7l2 binding by DamID. Using this platform at two genomic loci in mouse embryonic stem cells, we show that while the binding of Tcf7l2 closely follows the in vitro motif-binding strength and is influenced by local chromatin accessibility, it is also strongly affected by the surrounding 99 bp of sequence. Through controlled sequence perturbation, we show that Oct4 and Klf4 motifs promote Tcf7l2 binding, particularly in the adjacent ∼50 bp and oscillating with a 10.8-bp phasing relative to these cofactor motifs, which matches the turn of a DNA helix. © 2020 The Authors Transcription factor binding in cells depends on interactions with other proteins. We have developed a high-throughput screening platform to study transcription factor binding strength at thousands of variable sequences in a fixed genomic locus. We find that binding of the Wnt effector Tcf7l2 in mouse embryonic stem cells depends on proximity and phasing that matches the turn of the DNA helix relative to its cofactors Oct4 and Klf4. © 2020 The Authors, Cell Systems, 11 (3), ISSN:2405-4720

Single cell RNA-sequencing (scRNA-seq) technology has matured to the point that it is possible to generate large single cell atlases of developing mouse embryos. These atlases allow the dissection of developmental cell lineages and molecular changes during embryogenesis. When coupled with single cell technologies for profiling the chromatin landscape, epigenome, proteome and metabolome, and spatial tissue organisation, these scRNA-seq approaches can now collect a large volume of multi-omic data about mouse embryogenesis. In addition, advances in computational techniques have enabled the inference of developmental lineages of differentiating cells, even without explicitly introduced genetic markers. This Spotlight discusses recent advent of single cell experimental and computational methods, and key insights from applying these methods to the study of mouse embryonic development. We highlight challenges in analysing and interpreting these data to complement and expand our knowledge from traditional developmental biology studies in relation to cell identity, diversity and lineage differentiation.

Background Signaling pathways are the key biological mechanisms that transduce extracellular signals to affect transcription factor mediated gene regulation within cells. A number of computational methods have been developed to identify the topological structure of a specific signaling pathway using protein-protein interaction data, but they are not designed for identifying active signaling pathways in an unbiased manner. On the other hand, there are statistical methods based on gene sets or pathway data that can prioritize likely active signaling pathways, but they do not make full use of active pathway structure that link receptor, kinases and downstream transcription factors. Results Here, we present a method to simultaneously predict the set of active signaling pathways, together with their pathway structure, by integrating protein-protein interaction network and gene expression data. We evaluated the capacity for our method to predict active signaling pathways for dental epithelial cells, ocular lens epithelial cells, human pluripotent stem cell-derived lens epithelial cells, and lens fiber cells. This analysis showed our approach could identify all the known active pathways that are associated with tooth formation and lens development. Conclusions The results suggest that SPAGI can be a useful approach to identify the potential active signaling pathways given a gene expression profile. Our method is implemented as an open source R package, available via https://github.com/VCCRI/SPAGI/. Electronic supplementary material The online version of this article (10.1186/s12918-018-0655-x) contains supplementary material, which is available to authorized users.

A heart rate profile is constructed from continuous measurement of heart rate before, during and after exercise, typically in the context of an exercise stress test. It contains important clinical parameters such as resting heart rate, maximal heart rate during exercise, and decrease of heart rate in the recovery phase. An increase in resting heart rate, a decrease in heart rate elevation in response to exercise, and a delay in heart rate recovery are significant predictors of sudden death (Cole et al. 1999; Jouven et al. 2005). These measurements, when derived from a controlled exercise stress test regime, have high intra-individual reproducibility over 3 years, indicating their usefulness as a diagnostic tool (Orini et al. 2017). Heart rate parameters and profiles have been measured and constructed with exercise stress tests under controlled environments. This is often performed with a treadmill in a laboratory setting. With increasingly widespread availability of wearable wrist-worn fitness trackers, such as Fitbit and Apple Watch, we hypothesise that we can construct heart rate profiles for an individual using heart rate and physical activity (e.g. as measured by global positioning system and accelerator) data from a person’s wearable monitor. The main advantage of this approach is that it opens unprecedented opportunity to continuously and non-invasively monitor a person’s heart rate profile under free-living conditions, which would better match this person’s realistic activity profile. These devices are socially acceptable, generally inexpensive, and are already widely available in many communities. This potential cardiac function monitoring technology can be seamlessly incorporated into a user’s daily life, without the need for clinically based exercise stress testing. A number of recent studies have assessed the accuracy of popular wrist-worn wearable fitness trackers (Dooley et al. 2017; Shcherbina et al. 2017). They found that while energy expenditure estimations are often less accurate, the measurements of heart rate are generally considered accurate. These findings suggest that it should be possible to extract heart rate dynamics information before, during, and after an exercise event, which can be easily defined without accurate measurement of energy expenditure. Here, we present a new open source R package called CardiacProfileR. It is designed to efficiently extract heart rate and physical activity data from a Training Center XML (TCX) file which is generated by the most modern fitness tracking devices. CardiacProfileR can identify periods of active exercise from the data, and construct a heart rate profile for each period of active exercise. This package produces interactive visualisation of one or multiple heart rate profiles in two dimensions or three dimensions. This enables aggregation and comparison of data from multiple periods of exercise or comparison between multiple people. As far as we know, CardiacProfileR is the first open source R package that provides an end-to-end analysis framework for wearable fitness sensor data. CardiacProfileR is available via an MIT license at https://github.com/VCCRI/CardiacProfileR.

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCRαβ), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCRαβ reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (50 bp, while it failed for datasets with