Biology subjects

Ke, R.

Publications and source records attributed to Ke, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Photoactivatable Rac1: a photosensor tilt by time-resolved crystallography and a partially open switch I when GDP-bound

The LOV2 domain of Avena sativa phototropin 1 is widely used to control proteins with light, but how its signal reaches a fused effector has not been visualized. We followed photoactivatable Rac1 by time-resolved serial crystallography at room temperature. Within 350 {micro}s, a fifth of the photosensors form the flavin-cysteine adduct and tilt as a rigid body on Rac1 about an axis through the domain interface, while the J helix stays docked and Rac1 keeps GTP and dark-state switches up to 1.85 ms. Dark crystals contain a variable GDP-bound sub-population, so light data must be referenced to interleaved dark frames. In this sub-population, switch I is only partially open, held at a Ca2+ ion that bridges LOV2 and Rac1, a conformation that can persist in simulations without the lattice. The photosensor thus first acts on Rac1 at the interface that also holds switch I in the GDP-bound state.

biophysics↗

bioRxiv2026

A mechanistic basis for CD8+ T cell expansion sensitivity as a predictor of HIV post-treatment control

A key goal in HIV-1 cure research is to understand why some individuals control viral rebound after stopping antiretroviral therapy (ART). Recent human studies have identified responding CD8+ T cells expressing Ki-67 and the transcription factor TCF-1 as correlates of post-treatment control, but the mechanistic basis of this association remains unclear. Using the theoretical framework of Conway and Perelson, we fit mechanistic within-host models to viral load and CD8+ T cell data from 9 individuals in a combination immunotherapy trial following ART interruption. Although Ki-67 and TCF-1 measurements were not used for fitting, the inferred effector cell expansion sensitivity, i.e., the responsiveness of effector expansion to low antigen levels, shows a strong linear relationship with Ki-67 and TCF-1 levels at rebound (Pearsons r {approx} 0.8). Building on this, we show analytically that the post-rebound viral load set point is inversely proportional to the effector cell expansion sensitivity, and thus strongly correlates with cycling (Ki-67+) CD8+ T cells (r {approx} -0.8) at rebound, and a subset that expresses TCF-1 (r {approx} -0.9). In effect, individuals with a larger proportion of CD8+ T cells responding to viral rebound, and a greater representation of TCF-1 expressing cells within the responding subset, achieve markedly lower viral set points through a higher effector cell expansion sensitivity. This mechanism is consistent with prior modeling in a non-intervention ATI setting, suggesting it may generalize across more rebound contexts. Our results provide a mechanistic explanation why both Ki-67+ responding CD8+ T cells and their TCF-1-expressing subset predict post-treatment control, linking clinical correlation to its underlying cause and highlighting Ki-67 and TCF-1 as potential early biomarkers of HIV immunotherapy success.

immunology↗

bioRxiv2026

Covalent bond formation caught in a LOV photoreceptor

Light-oxygen-voltage (LOV) domains are blue-light photoreceptors of plants, algae and fungi, and among the most widely used tools in optogenetics. They switch on by forming a covalent thioether bond between a conserved cysteine and their flavin chromophore, in a reaction that needs a proton to cross from the cysteine to the flavin through a pocket containing essentially no water. Its mechanism has been debated for two decades1, and because the chemistry is over within a microsecond its elementary steps have stayed hidden. Here we combine 10 time-resolved serial femtosecond crystallography snapshots and infrared spectroscopy with QM/MM calculations to resolve the entire sequence of events at 1.4 [A] resolution: from excited-state distortion of the flavin ring (10-100 ps), through hydration of a surface channel (10 ns) and a single ordered water reaching the active site as the reactive cysteine shifts between its conformations (100-500 ns), to the thioether bond itself, caught half-formed at 1 {micro}s (half the molecules reacted, half still poised) and complete at 10-100 {micro}s. That water bridges the cysteine and the flavin and shuttles the proton, lowering the barrier from [~]35 to [~]15 kcal/mol and accelerating the reaction by roughly fourteen orders of magnitude (without it, the half-life would be [~]237,000 years), then departs before the bond forms. Proteins can therefore hydrate a dehydrated active site transiently and on demand to overcome otherwise prohibitive reaction barriers, a catalytic strategy that reaches well beyond photoreceptors.

biophysics↗

bioRxiv2026

Ribosome-tethered in situ sequencing for single-cell translatome analysis

Translational regulation plays a critical role in shaping cellular states and functions, yet approaches for spatially resolved translatome profiling at single-cell resolution remain limited. Here, we develop Ribosome-tethered In Situ Sequencing (Ribo-ISS), an imaging-based spatial translatomics technology that enables high-throughput mapping of ribosome-associated mRNAs in intact tissues. Ribo-ISS integrates ribosome-dependent molecular anchoring with multiplexed in situ sequencing, allowing specific detection of translation-associated transcripts without genetic manipulation or exogenous ribosome labeling. We demonstrate that Ribo-ISS achieves high specificity and enables single-cell spatial translatome profiling in mouse brain, accurately recapitulating major cell types and their anatomical organization. Applied to a sleep deprivation model, Ribo-ISS simultaneously resolved transcriptional and translational changes, revealing extensive transcription-translation uncoupling and distinct cell-type-specific translational responses. Ribo-ISS provides a scalable framework for investigating spatially organized translational programs and expands the capability of spatial omics toward understanding gene regulation beyond transcription. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=164 SRC="FIGDIR/small/740970v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@40da8dorg.highwire.dtl.DTLVardef@1cc76b4org.highwire.dtl.DTLVardef@8535c8org.highwire.dtl.DTLVardef@18e009d_HPS_FORMAT_FIGEXP M_FIG C_FIG Ribo-ISS integrates ribosome-dependent molecular anchoring with multiplexed in situ sequencing, enabling specific detection of ribosome-bound transcripts in intact tissues without genetic manipulation or exogenous labeling. As an imaging-based spatial translatomics technology, it resolves translatome profiling at single-cell resolution. This scalable approach reveals spatial translational programs, extending spatial omics beyond transcription.

molecular biology↗

bioRxiv2026

AZD5582 robustly reactivates latently infected cells and clears the majority of those reactivated from the SIV reservoir

AZD5582 (AZD) is a latency reversing agent used to support the "shock-and-kill" strategy in HIV-1 cure research. Previous studies in ART-suppressed rhesus macaques have shown that AZD can promote reactivation of latently infected cells, resulting in 2-3 log increases in on-ART viral load and significant reductions in SIV reservoir size over 5-10 doses. To quantify the impact of AZD on the reservoir, we developed an ensemble of mechanistic viral dynamic models and fit them to longitudinal plasma SIV RNA and SIV CA-DNA data from 23 macaques treated with AZD in combination with other therapies. The aggregate predictions of the model ensemble recapitulate the reactivation patterns observed in both SIV RNA and SIV CA-DNA and provide robust estimates of key parameters associated with reactivated cells. We found that AZD reactivates approximately 25% of cells in the latent reservoir per dose, with a mean reactivation duration of about 5-6 days. Of the reactivated cells, 60-79% are eventually cleared, while the remainder enter a state refractory to AZD stimulation before returning to latency. Because of this refractory state, each consecutive weekly dose reactivates about 28% fewer cells than the previous one, an effect that could be more pronounced if the refractory period substantially exceeds the interval between doses. However, the duration of this refractory state remains uncertain. Altogether, our results suggest that AZD-reactivated cells are effectively cleared. Future work should focus on improving LRAs that safely reactivate a larger fraction of the latent reservoir. Furthermore, designing experiments with varying dosing schedules can help better quantify the duration of refractoriness, which will be important for informing optimal treatment schedules and maximizing the effect of LRAs.

microbiology↗

bioRxiv2025

Efficient spatial gene expression profiling using split-probe ligation and rolling circle amplification

Spatial transcriptomics has transformed our understanding of gene regulation by enabling high-resolution mapping of RNA molecules within their native cellular and tissue environments. This is typically accomplished by capturing or imaging RNA in situ, thereby preserving spatial context. Here, we introduce an in situ RNA imaging method based on split-probe ligation and rolling circle amplification (RCA) for profiling spatial gene expression. In this approach, split-probes hybridize to adjacent regions of a target RNA fragment and are then enzymatically ligated to form circular DNA templates, which are subsequently amplified via RCA to boost signal. We demonstrate that this method enables robust in situ RNA detection and genotyping in both tissue sections and whole-mount tissue samples. By coupling this technique with in situ sequencing, we mapped the spatial expression patterns of 82 genes in the kidneys of healthy and diabetic male and female mice. This analysis revealed distinct localization of Aqp4 in proximal tubules and principal cells of the collecting ducts, and uncovered sex-specific transcriptomic alterations in diabetic kidneys with spatial resolution. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/668044v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@c8efaborg.highwire.dtl.DTLVardef@1eef010org.highwire.dtl.DTLVardef@1d2bbd7org.highwire.dtl.DTLVardef@2a482b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Integration of the hammerhead ribozyme into structured RNAs to measure ligand-binding events for riboswitch candidates and aptamers

Some structured RNAs, such as riboswitches and aptamers, can bind their cognate ligands and have been used in biosensors and gene expression control elements. However, current methods for detecting ligand binding to structured RNAs are either severely limited or inconvenient. In this study, we design a multi-base pair bridge to integrate a hammerhead ribozyme into structured RNAs to detect ligand binding events. The experimental results demonstrate that the length of the bridge has a significant effect on the cleavage of the ribozyme; optimal cleavage can be achieved with three to six base pairs in the bridge. The dissociation constant (KD) values obtained through this method are in agreement with those determined by in-line probing techniques, and 1 pmol of allosteric ribozyme RNA is sufficient for measurement. We apply this method to evaluate the binding affinity of the riboswitch candidate Motif_9307. Our findings indicate that this motif has no binding affinity for S-adenosylmethionine or several other tested ligands, which is consistent with the results of the in-line probing experiments. Notably, our method reveals an increase in cleavage activity when yeast extract is added as a mixture of ligands, suggesting that the ligand of Motif_9307 is present in the extract. In conclusion, we develop an alternative approach for measuring ligand binding events associated with riboswitch candidates and aptamers.

molecular biology↗

bioRxiv2024

Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion

Most people living with HIV-1 experience rapid viral rebound once antiretroviral therapy is interrupted; however, a small fraction remain in viral remission for an extended duration. Understanding the factors that determine whether viral rebound is likely after treatment interruption can enable the development of optimal treatment regimens and therapeutic interventions to potentially achieve a functional cure for HIV-1. We built upon the theoretical framework proposed by Conway and Perelson to construct dynamic models of virus-immune interactions to study factors that influence viral rebound dynamics. We evaluated these models using viral load data from 24 individuals following antiretroviral therapy interruption. The best-performing model accurately captures the heterogeneity of viral dynamics and highlights the importance of the effector cell expansion rate. Our results show that post-treatment controllers and non-controllers can be distinguished based on the effector cell expansion rate in our models. Furthermore, these results demonstrate the potential of using dynamic models incorporating an effector cell response to understand early viral rebound dynamics post-antiretroviral therapy interruption.

immunology↗

bioRxiv2024

Influenza A Viral Burst Size from Thousands of Infected Single Cells Using Droplet Quantitative PCR (dqPCR)

An important aspect of how viruses spread and infect is the viral burst size, or the number of new viruses produced by each infected cell. Surprisingly, this value remains poorly characterized for influenza A virus (IAV), commonly known as the flu. In this study, we screened tens of thousands of cells using a microfluidic method called droplet quantitative PCR (dqPCR). The high-throughput capability of dqPCR enabled the measurement of a large population of infected cells producing progeny virus. By measuring the fully assembled and successfully released viruses from these infected cells, we discover that the viral burst sizes for both the seasonal H3N2 and the 2009 pandemic H1N1 strains vary significantly, with H3N2 ranging from 101 to 104 viruses per cell, and H1N1 ranging from 101 to 103 viruses per cell. Some infected cells produce average numbers of new viruses, while others generate extensive number of viruses. In fact, we find that only 10% of the single-cell infections are responsible for creating a significant portion of all the viruses. This small fraction produced approximately 60% of new viruses for H3N2 and 40% for H1N1. On average, each infected cell of the H3N2 flu strain produced 709 new viruses, whereas for H1N1, each infected cell produced 358 viruses. This novel method reveals insights into the flu virus and can lead to improved strategies for managing and preventing the spread of viruses. Author summaryViruses infect and exploit host cells to reproduce and spread. The viral burst size, or the number of viral particles released from an infected cell, plays a critical role in understanding infection dynamics and overall viral fitness. However, accurately determining burst size for many single cells using conventional laboratory methods can be challenging. Here, we introduce dqPCR, a droplet microfluidic method for the rapid measurement of influenza virus numbers produced by thousands of individual cells. Our findings revealed that only a small proportion of infected cells are responsible for producing a significant portion of the total viral population. By utilizing this method in future studies, we can gain a deeper understanding of the role of diversity in rapidly evolving viruses.

bioengineering↗

bioRxiv2024

Stratification of viral shedding patterns in saliva of COVID-19 patients

Living with COVID-19 requires continued vigilance against the spread and emergence of variants of concern (VOCs). Rapid and accurate saliva diagnostic testing, alongside basic public health responses, is a viable option contributing to effective transmission control. Nevertheless, our knowledge regarding the dynamics of SARS-CoV-2 infection in saliva is not as advanced as our understanding of the respiratory tract. Here we analyzed longitudinal viral load data of SARS-CoV-2 in saliva samples from 144 patients with mild COVID-19 (a combination of our collected data and published data). Using a mathematical model, we quantified individual-level viral dynamics and stratified them into three groups using a clustering approach. Notably, the three groups exhibited distinct differences viral RNA detection durations: 11.5 days (95% CI: 10.6 to 12.4), 17.4 days (16.6 to 18.2), and 30.0 days (28.1 to 31.8), respectively. Surprisingly, this stratified grouping remained unexplained despite our analysis of 47 types of clinical data, including basic demographic information, clinical symptoms, results of blood tests, and vital signs. Additionally, we quantified the expression levels of 92 micro-RNAs in a subset of saliva samples, but these also failed to explain the observed stratification, although the mir-1846 level may have been weakly correlated with peak viral load. Our study provides insights into SARS-CoV-2 infection dynamics in saliva, highlighting the challenges in predicting the duration of viral RNA detection without indicators that directly reflect an individuals immune response, such as antibody induction. Given the significant individual heterogeneity in the kinetics of saliva viral shedding, identifying biomarker(s) for viral shedding patterns will be crucial for improving public health interventions in the era of living with COVID-19.

microbiology↗

bioRxiv2023

Predicting Impacts of Contact Tracing on Epidemiological Inference from Phylogenetic Data

Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007-2008 Swedish HIV-1 outbreak and the broader 1998-2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at github.com/MolEvolEpid/SEEPS.

bioinformatics↗

bioRxiv2023

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection. Author summaryMonoclonal antibodies have been used as a treatment for SARS-CoV-2. However, viral evolution and development of variants has compromised the use of all currently authorized monoclonal antibodies for SARS-CoV-2. In some individuals treated with one such monoclonal antibody, bamlanivimab, transient nasal viral rebounds of 3-4 logs associated with resistant viral strains occur. To better understand the mechanisms underlying resistance emergence with high viral load rebounds, we developed two different models that incorporate drug sensitive and drug resistant virus as well as target cell replenishment and fit them to data. The models accurately capture the observed viral dynamics as well as the proportion of resistant virus for each studied individual with little variation in model parameters. In the models with best-fit parameters, bamlanivimab selects for resistance mutants that can expand to high levels due to target cell replenishment. The ultimate clearance of virus however depends on the development of adaptive immunity.

immunology↗

bioRxiv2023

How reliable are estimates of key parameters in viral dynamic models?

Mathematical models of viral infection have been developed and fit to data to gain insight into disease pathogenesis for a number of agents including HIV, hepatitis C and B virus. However, for acute infections such as influenza and SARS-CoV-2, as well as for infections such as hepatitis C and B that can be acute or progress to being chronic, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the exponential phase of viral growth, i.e., when most transmission events occur. Missing data may make estimation of the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. Here, we evaluated the reliability of estimates of key model parameters when viral load data prior to the viral load peak is missing. We estimated the time from infection to peak viral load by fitting non-linear mixed models to a dataset with frequent viral RNA measurements, including pre-peak. We quantified the reliability of estimated infection times, key model parameters, and the time to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. We find a lack of data in the exponential growth phase underestimates the time to peak viral load by several days leading to a shorter predicted exponential growth phase. On the other hand, having an idea of the time of infection and fixing it, results in relatively good estimates of dynamical parameters even in the absence of early data.

immunology↗

bioRxiv2022

Improved in situ sequencing for high-resolution targeted spatial transcriptomic analysis in tissue sections

Spatial transcriptomics enables the study of localization-indexed gene expression activity in tissues, providing the transcriptional landscape that in turn indicates the potential regulatory networks of gene expression. In situ sequencing (ISS) is a targeted spatial transcriptomic technique, which is based on padlock probe and rolling circle amplification combined with next-generation sequencing chemistry, for highly multiplexed in situ gene expression profiling. Here, we present improved in situ sequencing (IISS) that exploits a new probing and barcoding approach, combined with advanced image analysis pipeline for high-resolution targeted spatial gene expression profiling. We developed an improved combinatorial probe anchor ligation chemistry using a 2-base encoding strategy for barcode interrogation. The new encoding strategy resulted in higher signal intensity as well as improved specificity for in situ sequencing, while maintaining a streamlined analysis pipeline for targeted spatial transcriptomics. We showed that IISS can be applied to both fresh frozen tissue and formalin-fixed paraffin embedded tissue sections for single cell level spatial gene expression analysis, based on which the developmental trajectory and cell-cell communication networks can also be constructed. In conclusion, our method is a versatile molecular tool for targeted spatial transcriptomic analysis.

molecular biology↗

bioRxiv2021

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0[≥] 3, and overall a specificity exceeding 85% and sensitivity better than 70%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification. Author summaryThe analysis of pathogen genomic data to analyze epidemics at scale is constrained by the computational cost associated with phylogenetic tree reconstruction. As a fast and efficient alternative, we employed convolutional neural networks to analyze evolutionary pairwise distance matrices as images to perform classifications of the current epidemiological situation of a growing public health sequence database. We used simulated data to train and test our model, and as validation we accurately mapped the start and end of two linked well-documented HIV-1 outbreaks in the backdrop of ongoing slower HIV spread. Thus, our new approach is efficient, accurate, scalable, and can analyze data in real time.

bioinformatics↗

bioRxiv2021

Polymerase chain reaction-based assays facilitate the breeding and study of mouse models of Klinefelter syndrome

Klinefelter syndrome (KS) is one of the most frequent genetic abnormalities and the leading genetic cause of non-obstructive azoospermia. The breeding of mouse models of KS and their study are essential to advance our knowledge of the pathologic mechanism. Karyotyping and fluorescence in situ hybridization are reliable methods for identifying chromosomal contents. However, technical issues associated with these methods can decrease the efficiency of breeding KS mouse models and limit studies that require rapid identification of target mice. To overcome these limitations, we developed three polymerase chain reaction-based assays to measure the specific genetic information, including the presence or absence of Sry, copy number of Amelx, and Xist RNA transcript levels. Through a combined analysis of the assay results, we can infer the karyotype of target mice. We confirmed the utility of our assays with the successful generation of KS mouse models. Our assays are rapid, inexpensive, high capacity, easy to perform, and require small amounts of sample. Therefore, they facilitate the breeding and study of KS mouse models and help advance our knowledge of the pathologic mechanism underlying KS.

genetics↗

bioRxiv2020

IRIS: an accurate and efficient barcode calling tool for in situ sequencing

SummaryThe emerging in situ RNA sequencing technologies which can capture and amplify RNA within the original tissues provides efficient solution for producing spatial expression map from dozens to thousands of genes. Most of in situ RNA-seq strategies developed recently infer the expression patterns based on the fluorescence signals from the images taken during sequencing. However, an automate and convenient tool for decoding signals from image information is still absent. Here we present an easy-to-use software named IRIS to efficiently decode image signals from in situ sequencing into nucleotide sequences. This software can record the quality score and the spatial information of the sequencing signals. We also develop an interactive R shiny app named DAIBC for data visualization. IRIS is designed in modules so that it could be easily extended and compatible to new technologies. Availability and implementationIRIS and DAIBC are freely available under BSD 3-Clause License at: https://github.com/th00516/ISS_pyIRIS. Contactguojie.zhang@bio.ku.dk

bioinformatics↗

bioRxiv2019

A common pattern of influenza A virus single cell gene expression heterogeneity governs the innate antiviral response to infection

Viral infection outcomes are governed by the complex and dynamic interplay between the infecting virus population and the host response. It is increasingly clear that both viral and host cell populations are highly heterogeneous, but little is known about how this heterogeneity influences infection dynamics or viral pathogenicity. To dissect the interactions between influenza A virus (IAV) and host cell heterogeneity, we examined the combined host and viral transcriptomes of thousands of individual, single virion-infected cells. We observed complex patterns of viral gene expression and the existence of multiple distinct host transcriptional responses to infection at the single cell level. Our analyses reveal that viral NS segment gene expression diverges from that of the rest of the viral genome within a subset of infected cells, and that this unique pattern of NS segment expression can play a dominant role in shaping the host cell response to infection. Finally, we show that seasonal human H1N1 and H3N2 strains differ significantly in patterns of host anti-viral gene transcriptional heterogeneity at the single cell level. Altogether, these data reveal a common pattern of viral gene expression heterogeneity across human IAV subtypes that can serve as a major determinant of antiviral gene activation.

microbiology↗