Multiple Experimental Conditions Research Articles

FlexStat: Combinatory differentially expressed protein extraction

Abstract Motivation Mass spectrometry-based system proteomics allows identification of dysregulated protein hubs and associated disease-related features. Obtaining differentially expressed proteins (DEPs) is the most important step of downstream bioinformatics analysis. However, the extraction of statistically significant DEPs from datasets with multiple experimental conditions or disease types through currently available tools remains a laborious task. More often such an analysis requires considerable bioinformatics expertise, making it inaccessible to researchers with limited computational analytics experience. Results To uncover the differences among the many conditions within the data in a user-friendly manner, here we introduce FlexStat, a web-based interface that extracts DEPs through combinatory analysis. This tool accepts a protein expression matrix as input and systematically generates DEP results for every conceivable combination of various experimental conditions or disease types. FlexStat includes a suite of robust statistical tools for data preprocessing, in addition to DEP extraction, and publication-ready visualization, which are built on established R scientific libraries in an automated manner. This analytics suite was validated in diverse public proteomic datasets to showcase its high performance of rapid and simultaneous pairwise comparisons of comprehensive data sets. Availability and implementation FlexStat is implemented in R and is freely available at https://jglab.shinyapps.io/flexstatv1-pipeline-only/. The source code is accessible at https://github.com/kts-desilva/FlexStat/tree/main. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials

We address the high accuracy and precision demands for analyzing large in situ or in operando spectral data sets. A dual-input artificial neural network (ANN) algorithm enables the compositional and depth-sensitive analysis of multinary materials by simultaneously evaluating spectra collected under multiple experimental conditions. To validate the developed algorithm, a case study was conducted analyzing complex Rutherford backscattering spectrometry (RBS) spectra collected in two scattering geometries. The dual-input ANN analysis excelled in providing a systematic analysis and precise results, showcasing its robustness in handling complex data and minimizing user bias. A comprehensive comparison with human supervision analysis and conventional single-input ANN analysis revealed a reduced susceptibility of the dual-input ANN analysis to inaccurately known setup parameters, a common challenge in material characterization. The developed multi-input approach can be extended to a wide range of analytical techniques, in which the combined analysis of measurements performed under different experimental conditions is beneficial for disentangling details of the material properties.

RNAvigate: efficient exploration of RNA chemical probing datasets.

Chemical probing technologies enable high-throughput examination of diverse structural features of RNA, including local nucleotide flexibility, RNA secondary structure, protein and ligand binding, through-space interaction networks, and multistate structural ensembles. Deep understanding of RNA structure-function relationships typically requires evaluating a system under structure- and function-altering conditions, linking these data with additional information, and visualizing multilayered relationships. Current platforms lack the broad accessibility, flexibility and efficiency needed to iterate on integrative analyses of these diverse, complex data. Here, we share the RNA visualization and graphical analysis toolset RNAvigate, a straightforward and flexible Python library that automatically parses 21 standard file formats (primary sequence annotations, per- and internucleotide data, and secondary and tertiary structures) and outputs 18 plot types. RNAvigate enables efficient exploration of nuanced relationships between multiple layers of RNA structure information and across multiple experimental conditions. Compatibility with Jupyter notebooks enables nonburdensome, reproducible, transparent and organized sharing of multistep analyses and data visualization strategies. RNAvigate simplifies and accelerates discovery and characterization of RNA-centric functions in biology.

Machine learning modeling and additive explanation techniques for glutathione production from multiple experimental growth conditions of Saccharomyces cerevisiae

Glutathione (GSH) production is of great industrial interest due to its essential properties. This study aimed to use machine learning (ML) methods to model GSHproduction under different growth conditions of Saccharomyces cerevisiae, namely cultivation time, culture volume, pressure, and magnetic field application. Different ML and regression models were evaluated for their statistics to select the most robust model. Results showed that eXtreme Gradient Boosting (XGB) was the best predictive performance model. From the best model, additive explanation techniques were used to identify the feature importance of process. According to variable analysis, the best conditions to obtain the highest GSH concentrations would be cultivation times of 72–96 h, low magnetic field intensity (3.02 mT), low pressure (0.5 kgf.cm−2), and high culture volume (3.5 L). XGB use and additive explanation techniques proved promising for determining process optimization conditions and selecting the essential process variables.

Targeted mapping and utilization of the perihepatic surface for therapeutic beta cell replacement and retrieval in diabetic non-human primates

IntroductionSuccessful diabetes reversal using pancreatic islet transplantation by various groups illustrates the significant achievements made in cell-based diabetes therapy. While clinically, intraportal islet delivery is almost exclusively used, it is not without obstacles, including instant blood-mediated inflammatory reaction (IBMIR), relative hypoxia, and loss of function over time, therefore hindering long-term success. Here we demonstrate the perihepatic surface of non-human primates (NHPs) as a potential islet delivery site maximizing favorable characteristics, including proximity to a dense vascular network for adequate oxygenation while avoiding IBMIR exposure, maintenance of portal insulin delivery, and relative ease of accessibility through minimally invasive surgery or percutaneous means. In addition, we demonstrate a targeted mapping technique of the perihepatic surface, allowing for the testing of multiple experimental conditions, including a semi-synthetic hydrogel as a possible three-dimensional framework to improve islet viability.MethodsPerihepatic allo-islet cell transplants were performed in immunosuppressed cynomolgus macaques using a targeted mapping technique to test multiple conditions for biocompatibility. Transplant conditions included islets or carriers (including hydrogel, autologous plasma, and media) alone or in various combinations. Necropsy was performed at day 30, and histopathology was performed to assess biocompatibility, immune response, and islet viability. Subsequently, single-injection perihepatic allo-islet transplant was performed in immunosuppressed diabetic cynomolgus macaques. Metabolic assessments were measured frequently (i.e., blood glucose, insulin, C-peptide) until final graft retrieval for histopathology.ResultsTargeted mapping biocompatibility studies demonstrated mild inflammatory changes with islet-plasma constructs; however, significant inflammatory cell infiltration and fibrosis were seen surrounding sites with the hydrogel carrier affecting islet viability. In diabetic NHPs, perihepatic islet transplant using an autologous plasma carrier demonstrated prolonged function up to 6 months with improvements in blood glucose, exogenous insulin requirements, and HbA1c. Histopathology of these islets was associated with mild peri-islet mononuclear cell infiltration without evidence of rejection.DiscussionThe perihepatic surface serves as a viable site for islet cell transplantation demonstrating sustained islet function through 6 months. The targeted mapping approach allows for the testing of multiple conditions simultaneously to evaluate immune response to biomaterials at this site. Compared to traditional intraportal injection, the perihepatic site is a minimally invasive approach that allows the possibility for graft recovery and avoids IBMIR.

Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis.

Although metabolomics data acquisition and analysis technologies have become increasingly sophisticated over the past 5-10 years, deciphering a metabolite's function from a description of its structure and its abundance in a given experimental setting is still a major scientific and intellectual challenge. To point out ways to address this "data to knowledge" challenge, we developed a functional metabolomics strategy that combines state-of-the-art data analysis tools and applied it to a human scalp metabolomics data set: skin swabs from healthy volunteers with normal or oily scalp (Sebumeter score 60-120, n = 33; Sebumeter score > 120, n = 41) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), yielding four metabolomics data sets for reversed phase chromatography (C18) or hydrophilic interaction chromatography (HILIC) separation in electrospray ionization (ESI) + or - ionization mode. Following our data analysis strategy, we were able to obtain increasingly comprehensive structural and functional annotations, by applying the Global Natural Product Social Networking (M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, et al., Nat Biotechnol 34:828-837, 2016, https://doi.org/10.1038/nbt.3597), SIRIUS (K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, et al., Nat Methods 16:299-302, 2019, https://doi.org/10.1038/s41592-019-0344-8), and MicrobeMASST (S. ZuffaS, R. Schmid, A. Bauermeister, P. W, P. Gomes, et al., bioRxiv:rs.3.rs-3189768, 2023, https://doi.org/10.21203/rs.3.rs-3189768/v1) tools. We finally combined the metabolomics data with a corresponding metagenomic sequencing data set using MMvec (J. T. Morton, A. A. Aksenov, L. F. Nothias, J. R. Foulds, et. al., Nat Methods 16:1306-1314, 2019, https://doi.org/10.1038/s41592-019-0616-3), gaining insights into the metabolic niche of one of the most prominent microbes on the human skin, Staphylococcus epidermidis.IMPORTANCESystems biology research on host-associated microbiota focuses on two fundamental questions: which microbes are present and how do they interact with each other, their host, and the broader host environment? Metagenomics provides us with a direct answer to the first part of the question: it unveils the microbial inhabitants, e.g., on our skin, and can provide insight into their functional potential. Yet, it falls short in revealing their active role. Metabolomics shows us the chemical composition of the environment in which microbes thrive and the transformation products they produce. In particular, untargeted metabolomics has the potential to observe a diverse set of metabolites and is thus an ideal complement to metagenomics. However, this potential often remains underexplored due to the low annotation rates in MS-based metabolomics and the necessity for multiple experimental chromatographic and mass spectrometric conditions. Beyond detection, prospecting metabolites' functional role in the host/microbiome metabolome requires identifying the biological processes and entities involved in their production and biotransformations. In the present study of the human scalp, we developed a strategy to achieve comprehensive structural and functional annotation of the metabolites in the human scalp environment, thus diving one step deeper into the interpretation of "omics" data. Leveraging a collection of openly accessible software tools and integrating microbiome data as a source of functional metabolite annotations, we finally identified the specific metabolic niche of Staphylococcus epidermidis, one of the key players of the human skin microbiome.

Controlled hydrogel-based encapsulation of macrophages determines cell survival and functionality upon cryopreservation

The development of novel cell-based therapies has increased the necessity to improve the long-term storage of cells. The current method of cryopreservation is far from optimal, causing ice-associated mechanical and osmotic damage to sensitive cells. Cell encapsulation is emerging as a new strategy to overcome those current limitations; however, few data are applicable to slow freezing, with conflicting results and multiple experimental conditions. The objective of this research work was to evaluate the impact of capsule size and encapsulation method on cell survival and functionality after a conventional freezing protocol. To this end, cells were encapsulated in alginate beads of different sizes, spanning the range of 200–2000 µm thanks to multiple extrusion techniques and conditions, and further cryopreserved using a slow cooling rate (−1°C/min) and 10 % DMSO as cryoprotectant. Our data show that there is a strong correlation between bead size and cell survival after a slow cooling cryopreservation process, with cell viabilities ranging from 7 to 70 % depending on the capsule size, with the smallest capsules (230 µm) achieving the highest level of survival. The obtained results indicate that the beads’ diameter, rather than their morphology or the technique used, plays a significant role in the post-thawing cell survival and functionality. These results show that a fine control of cell encapsulation in alginate hydrogels is required when it comes to overcoming the current limitations of long-term preservation techniques by slow cooling.

Multiplexed experimental strategies for fragment library screening against challenging drug targets using SPR biosensors

Surface plasmon resonance (SPR) biosensor methods are ideally suited for fragment-based lead discovery. However, generally applicable experimental procedures and detailed protocols are lacking, especially for structurally or physico-chemically challenging targets or when tool compounds are not available. Success depends on accounting for the features of both the target and the chemical library, purposely designing screening experiments for identification and validation of hits with desired specificity and mode-of-action, and availability of orthogonal methods capable of confirming fragment hits. The range of targets and libraries amenable to an SPR biosensor-based approach for identifying hits is considerably expanded by adopting multiplexed strategies, using multiple complementary surfaces or experimental conditions. Here we illustrate principles and multiplexed approaches for using flow-based SPR biosensor systems for screening fragment libraries of different sizes (90 and 1056 compounds) against a selection of challenging targets. It shows strategies for the identification of fragments interacting with 1) large and structurally dynamic targets, represented by acetyl choline binding protein (AChBP), a Cys-loop receptor ligand gated ion channel homologue, 2) targets in multi protein complexes, represented by lysine demethylase 1 and a corepressor (LSD1/CoREST), 3) structurally variable or unstable targets, represented by farnesyl pyrophosphate synthase (FPPS), 4) targets containing intrinsically disordered regions, represented by protein tyrosine phosphatase 1B (PTP1B), and 5) aggregation-prone proteins, represented by an engineered form of human tau (tau K18M). Practical considerations and procedures accounting for the characteristics of the proteins and libraries, and that increase robustness, sensitivity, throughput and versatility are highlighted. The study shows that the challenges for addressing these types of targets is not identification of potentially useful fragments per se, but establishing methods for their validation and evolution into leads.

Diagnostic Performance of an Eye-Tracking Assisted Visual Inference Language Test in the Assessment of Cognitive Decline due to Alzheimer's Disease.

The assessment of language changes associated with visual search impairment can be an important diagnostic tool in the Alzheimer's disease (AD) continuum. Investigate the performance of an eye-tracking assisted visual inference language task in differentiating subjects with mild cognitive impairment (MCI) or AD dementia from cognitively unimpaired older adults (controls). We assessed a group of 95 older adults (49 MCI, 18 mild dementia due to AD, and 28 controls). The subjects performed the same task under multiple experimental conditions which generate correlated responses that need to be taken into account. Thus, we performed a non-parametric repeated measures ANOVA model for verbal answers, and a linear mixed model (LMM) or its generalized version for the analysis of eye tracking variables. Significant differences were found in verbal answers across all diagnostic groups independently of type of inference, i.e., logic or pragmatic. Also, eye-tracking parameters were able to discriminate AD from MCI and controls. AD patients did more visits to challenge stimulus (Control-AD, -0.622, SE = 0.190, p = 0.004; MCI-AD, -0.514, SE = 0.173, p = 0.011), more visits to the correct response stimulus (Control-AD, -1.363, SE = 0.383, p = 0.002; MCI-AD, -0.946, SE = 0.349, p = 0.022), more fixations on distractors (Control-AD, -4.580, SE = 1.172, p = 0.001; MCI-AD, -2.940, SE = 1.070, p = 0.020), and a longer time to first fixation on the correct response stimulus (Control-AD, -0.622, SE = 0.190, p = 0.004; MCI-AD, -0.514, SE = 0.173, p = 0.011). The analysis of oculomotor behavior along with language assessment protocols may increase the sensitivity for detection of subtle deficits in the MCI-AD continuum, representing an important diagnostic tool.

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling.

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.

Variability of the innate immune response is globally constrained by transcriptional bursting.

Transcription of almost all mammalian genes occurs in stochastic bursts, however the fundamental control mechanisms that allow appropriate single-cell responses remain unresolved. Here we utilise single cell genomics data and stochastic models of transcription to perform global analysis of the toll-like receptor (TLR)-induced gene expression variability. Based on analysis of more than 2000 TLR-response genes across multiple experimental conditions we demonstrate that the single-cell, gene-by-gene expression variability can be empirically described by a linear function of the population mean. We show that response heterogeneity of individual genes can be characterised by the slope of the mean-variance line, which captures how cells respond to stimulus and provides insight into evolutionary differences between species. We further demonstrate that linear relationships theoretically determine the underlying transcriptional bursting kinetics, revealing different regulatory modes of TLR response heterogeneity. Stochastic modelling of temporal scRNA-seq count distributions demonstrates that increased response variability is associated with larger and more frequent transcriptional bursts, which emerge via increased complexity of transcriptional regulatory networks between genes and different species. Overall, we provide a methodology relying on inference of empirical mean-variance relationships from single cell data and new insights into control of innate immune response variability.

DCATS: differential composition analysis for flexible single-cell experimental designs

Differential composition analysis — the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions — is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods.

Global Model Calibration of High-Temperature Gas-Cooled Reactor Pebble-Bed Module Using an Adaptive Experimental Design

The world’s first high-temperature gas-cooled reactor pebble-bed module (HTR-PM) nuclear power plant adopts an innovative reactor type and a modular structure design. Parameter estimation and model calibration are of great significance prior to the implementation of model-based control and optimization. This paper focuses on identifying the thermal hydraulic parameters of HTR-PM over the global operating domain. The process technology and model mechanism of HTR-PM are reviewed. A parameter submodel named global parameter mapping is presented to quantify the relationship between an unknown model parameter and different operating conditions in a data-driven manner. The ideal construction of such a mapping requires reliable estimates, a well-poised sample set and an appropriate global surrogate. An adaptive model calibration scheme is designed to tackle these three issues correspondingly. First, a systematic parameter estimation approach is developed to ensure reliable estimates via heuristic subset selection consisting of estimability analysis and reliability evaluation. To capture the parameter behavior among the multiple experimental conditions and meanwhile reduce the operating cost, an adaptive experimental design is employed to guide condition testing. Experimental conditions are sequentially determined by comprehensively considering the criteria of sampling density, local nonlinearity and parameter uncertainty. Support vector regression is introduced as the global surrogate due to its capability of small-sample learning. Finally, the effectiveness of the model calibration scheme and its application performance in HTR-PM are validated by the simulation results.

Integrating intracellular and extracellular proteomic profiling for in-depth investigations of cellular communication in a model of prostate cancer.

Cellular communication is essential for cell-cell interactions, maintaining homeostasis and progression of certain disease states. While many studies examine extracellular proteins, the holistic extracellular proteome is often left uncaptured, leaving gaps in our understanding of how all extracellular proteins may impact communication and interaction. We used a cellular-based proteomics approach to more holistically profile both the intracellular and extracellular proteome of prostate cancer. Our workflow was generated in such a manner that multiple experimental conditions can be observed with the opportunity for high throughput integration. Additionally, this workflow is not limited to a proteomic aspect, as metabolomic and lipidomic studies can be integrated for a multi-omics workflow. Our analysis showed coverage of over 8000 proteins while also garnering insights into cellular communication in the context of prostate cancer development and progression. Identified proteins covered a variety of cellular processes and pathways, allowing for the investigation of multiple aspects into cellular biology. This workflow demonstrates advantages for integrating intra- and extracellular proteomic analyses as well as potential for multi-omics researchers. This approach possesses great value for future investigations into the systems biology aspects of disease development and progression.

Optimized Methods for the Surface Immobilization of Collagens and Collagen Binding Assays.

Fibrosis occurs in various tissues as a reparative response to injury or damage. If excessive, however, fibrosis can lead to tissue scarring and organ failure, which is associated with high morbidity and mortality. Collagen is a key driver of fibrosis, with type I and type III collagen being the primary types involved in many fibrotic diseases. Unlike conventional protocols used to immobilize other proteins (e.g., elastin, albumin, fibronectin, etc.), comprehensive protocols to reproducibly immobilize different types of collagens in order to produce stable coatings are not readily available. Immobilizing collagen is surprisingly challenging because multiple experimental conditions may affect the efficiency of immobilization, including the type of collagen, the pH, the temperature, and the type of microplate used. Here, a detailed protocol to reproducibly immobilize and quantify type I and III collagens resulting instable and reproducible gels/films is provided. Furthermore, this work demonstrates how to perform, analyze, and interpret in vitro time-resolved fluorescence binding studies to investigate the interactions between collagens and candidate collagen-binding compounds (e.g., a peptide conjugated to a metal chelate carrying, for example, europium [Eu(III)]). Such an approach can be universally applied to various biomedical applications, includingthe field of molecular imaging to develop targeted imaging probes, drug development, cell toxicity studies, cell proliferation studies, and immunoassays.

Shear stress shifts clathrin-coated vesicle formation to favor the flat-to-curved model in endothelial cells.

Endothelial cells (ECs) experience a variety of highly dynamic mechanical stresses. Among others, cyclic stretch and increased plasma membrane tension inhibit clathrin-mediated endocytosis (CME) in non-ECs cells. How ECs overcome such unfavorable conditions and maintain CME remains elusive. Previously, we have used simultaneous two-wavelength axial ratiometry (STAR) microscopy to resolve how clathrin-coated vesicles (CCVs) from in fibroblast-like cells (Cos-7) and human umbilical vein endothelial cells (HUVECs). This revealed multiple productive ways of CCV formation, supporting the flexible model of CME. We next asked whether biophysical stresses generated by blood flow could favor one mechanism of CCV formation to overcome unfavorable environment present in vasculature. Addressing this question required development of an automated MATLAB-based accelerated STAR data processing pipeline (DrSTAR) to enable the processing of multiple experimental conditions and biological replicates in a robust and reproducible environment. Moreover, DrSTAR employs a dynamic local referencing algorithm, which resolves the curvature of long-lasting CCSs. We used this new platform to examine if clathrin dynamics are altered in HUVECs grown under sheer stress. We found sheer stress led to an increase in clathrin dynamics. Surprisingly, we found this was due to an increase of flat clathrin accumulations in flow-stimulated cells, while the number of curved events remained consistent between groups. The curvature-positive events had significantly delayed curvature initiation in flow-stimulated cells, highlighting a shift toward flat-to-curved clathrin transitions in vesicle formation. Moreover, CME was more resistant to increased osmotic pressure in HUVECs grown under shear stress. Overall, our findings indicate that clathrin dynamics and CCV formation can be modulated by the local environment and represents an important regulatory mechanism.

Justice before Expediency: Robust Intuitive Concern for Rights Protection in Criminalization Decisions

The notion that a false positive (false conviction) is worse than a false negative (false acquittal) is a deep-seated commitment in the theory of criminal law. Its most illustrious formulation, the so-called Blackstone’s ratio, affirms that “it is better that ten guilty persons escape than that one innocent suffer”. Are people’s evaluations of criminal statutes consitent with this tenet of the Western legal tradition? To answer this question, we conducted three experiments (total N = 2492) investigating how people reason about a particular class of offenses—proxy crimes—known to vary in their specificity and sensitivity in predicting actual crime. By manipulating the extent to which proxy crimes convict the innocent and acquit those guilty of a target offense, we uncovered evidence that attitudes toward proxy criminalization depend primarily on its propensity toward false positives, with false negatives exerting a substantially weaker effect. This tendency arose across multiple experimental conditions—whether we matched the rates of false positives and false negatives or their frequencies, whether information was presented visually or numerically, and whether decisions were made under time pressure or after a forced delay—and was unrelated to participants’ probability literacy or their professed views on the purpose of criminal punishment. Despite the observed inattentiveness to false negatives, when asked to justify their decisions, participants retrospectively supported their judgments by highlighting the proxy crime’s efficacy (or inefficacy) in combating crime. These results reveal a striking inconsistency: people favor criminal policies that protect the rights of the innocent, but report comparable concern for their expediency in fighting crime.

Ultrasensitive and colorimetric small extracellular vesicles (sEVs) analysis via dual-cycle signal tool

For the clinical diagnosis of diseases and for basic biological research, it is crucial to develop a trustworthy and efficient method for detecting small extracellular vesicles (sEVs) in multiple experimental conditions. Here, we create a colorimetric assay that enables sensitive and precise sEVs identification without the need for pricey equipment. In this assay, the exonuclease III (Exo III)-assisted signal recycle is activated by the released single-strand DNA (ssDNA) from SMBs (streptavidin magnetic beads)-aptamer-ssDNA complex after identification of sEVs. By integrating with the strand displacement amplification (SDA) process, a significant amount of double-strand DNA products with G-rich tails is produced. The G-rich tails fold to G-quadruplex under the assistance of hemin to catalyze the oxidation of TMB, yielding a color change. The approach offers a broad detection range of 5 orders of magnitudes based on the signal recycles and SDA. In addition, single-stranded DNA binding protein (SSB) is exploited in this method to minimize the background signal from non-specific digestion of Exo-III, making the method a robust tool for sEVs detection and disease diagnosis.

Prediction of transcript structure and concentration using RNA-Seq data.

Alternative splicing (AS) is a key post-transcriptional modification that helps in increasing protein diversity. Almost 90% of the protein-coding genes in humans are known to undergo AS and code for different transcripts. Some transcripts are associated with diseases such as breast cancer, lung cancer and glioblastoma. Hence, these transcripts can serve as novel therapeutic and prognostic targets for drug discovery. Herein, we have developed a pipeline, Finding Alternative Splicing Events (FASE), as the R package that includes modules to determine the structure and concentration of transcripts using differential AS. To predict the correct structure of expressed transcripts in given conditions, FASE combines the AS events with the information of exons, introns and junctions using graph theory. The estimated concentration of predicted transcripts is reported as the relative expression in terms of log2CPM. Using FASE, we were able to identify several unique transcripts of EMILIN1 and SLK genes in the TCGA-BRCA data, which were validated using RT-PCR. The experimental study demonstrated consistent results, which signify the high accuracy and precision of the developed methods. In conclusion, the developed pipeline, FASE, can efficiently predict novel transcripts that are missed in general transcript-level differential expression analysis. It can be applied selectively from a single gene to simple or complex genome even in multiple experimental conditions for the identification of differential AS-based biomarkers, prognostic targets and novel therapeutics.

DrSTAR: Tracking real-time nanometer axial changes

Protein interactions with the plasma membrane mediate processes critical for cell viability such as migration and endocytosis, yet our understanding of how recruitment of key proteins correlates with their ability to sense or induce energetically unfavorable plasma membrane shapes remains limited. Simultaneous two-wavelength axial ratiometry (STAR) microscopy provides millisecond time resolution and nanometer axial resolution of protein dynamics at the basal plasma membrane. However, STAR microscopy requires extensive and time-consuming quantitative data processing to access axial (Δz) information. Therefore, addressing questions about the influence of biological and biophysical factors on the interaction between the plasma membrane and protein of interest remains challenging. Here, we overcome the limitations in STAR data processing and present dynamic reference STAR (DrSTAR): a user-friendly, automated, open-source MATLAB-based package. DrSTAR enables processing multiple experimental conditions and biological replicates, employs a novel local background referencing algorithm, and accelerates processing time to facilitate broad adaptation of STAR for studying nanometer axial changes in protein distribution.