Biology subjects

West, J.

Publications and source records attributed to West, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Development of FAP-targeted theranostics discovered by next-generation sequencing-augmented mining of a novel immunized VNAR library

Cancer-associated fibroblasts (CAFs) in the stroma of solid tumors promote an immunosuppressive tumor microenvironment (TME) that drives resistance to therapies. The expression of the protease fibroblast activation protein (FAP) on the surface of CAFs has made FAP a target for development of therapies to dampen immunosuppression. Relatively few biologics have been developed for FAP and none have been developed that exploit the unique engagement properties of Variable New Antigen Receptors (VNARs) from shark antibodies. As the smallest binding domain in nature, VNARs cleverage unique geometries and recognize epitopes conventional antibodies cannot. By directly immunizing a nurse shark with FAP, we created a large anti-FAP VNAR phage display library. This library allowed us to identify a suite of anti-FAP VNARs through traditional biopanning and also by an in silico approach that did not require any prior affinity-based enrichment in vitro. We investigated four VNAR-Fc fusion proteins for theranostic properties and found that all four recognized FAP with high affinity and were rapidly internalized by FAP-positive cells. As a result, the VNAR-Fc constructs were effective antibody-drug conjugates in vitro and were able to localize to FAP-positive xenografts in vivo. Our findings establish VNAR-Fc constructs as a versatile platform for theranostic development that could yield innovative cancer therapies targeting the TME.

synthetic biology↗

bioRxiv2024

A novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind

Chemotherapy remains a commonly used and important treatment option for metastatic breast cancer. A majority of ER+ metastatic breast cancer patients ultimately develop resistance to chemotherapy, resulting in disease progression. We hypothesized that an "evolutionary double-bind", where treatment with one drug improves the response to a different agent, would improve the effectiveness and durability of responses to chemotherapy. This approach exploits vulnerabilities in acquired resistance mechanisms. Evolutionary models can be used in refractory cancer to identify alternative treatment strategies that capitalize on acquired vulnerabilities and resistance traits for improved outcomes. To develop and test these models, ER+ breast cancer cell lineages sensitive and resistant to chemotherapy are grown in spheroids with varied initial population frequencies to measure cross-sensitivity and efficacy of chemotherapy and add-on treatments such as disulfiram combination treatment. Different treatment schedules then assessed the best strategy for reducing the selection of resistant populations. We developed and parameterized a game-theoretic mathematical model from this in vitro experimental data, and used it to predict the existence of a double-bind where selection for resistance to chemotherapy induces sensitivity to disulfiram. The model predicts a dose-dependent re-sensitization (a double-bind) to chemotherapy for monotherapy disulfiram.

cancer biology↗

bioRxiv2024

Evolutionary double-bind treatment using radiotherapy and NK cell-based immunotherapy in prostate cancer

Evolution-informed therapies exploit ecological and evolutionary consequences of drug resistance to inhibit the expansion of treatment-resistant populations and prolong time to progression. One strategy, termed an evolutionary double-bind, uses an initial therapy to elicit a specific adaptive response by the cancer cells, which is then selectively targeted by a follow-on therapy. Here we examine the combination of radiation therapy and immunotherapy as a quantifiable double-bind strategy. Radiotherapy (RT) induces lethal double-strand DNA breaks, but cancer cells can adapt by upregulating DNA damage response pathways. While this evolutionary strategy increases resistance to DNA damaging agents, it also results in enhanced expression of natural killer (NK) cell ligands potentially increasing vulnerability to an immune response. Using a radiation-resistant human prostate carcinoma cell line (22Rv1), we demonstrate that RT-resistant cells upregulate NK cell ligands, including major histocompatibility complex class I chain-related protein A/B (MICA/B), and poliovirus receptors (PVR1, PVRL2) with a 2-fold increase in sensitivity to NK cell mediated killing. We investigated this potential evolutionary double bind through in vitro studies and evolution-based mathematical models. Radiotherapy alone slowed overall growth but strongly selected for RT-resistant cells. NK cell therapy alone suppressed the RT-resistant population but with a surviving population of radiation-sensitive cells. These dynamics were framed mathematically, and model simulation predicted optimal tumour control would be achieved through initial RT rapidly followed by NK-based immunotherapy. Subsequent experiments confirmed the model prediction. We conclude that radiotherapy and NK cell-based immunotherapy produces an evolutionary double bind that can be exploited in heterogenous tumours to limit RT resistance.

cancer biology↗

bioRxiv2024

Droplet microfluidics for time-resolved serial crystallography

Serial crystallography requires large numbers of microcrystals and robust strategies to rapidly apply substrates to initiate reactions in time-resolved studies. Here we report the use of droplet miniaturisation for the controlled production of uniform crystals, providing an avenue for controlled diffusion and synchronous reaction initiation. The approach was evaluated using two enzymatic systems, yielding 3-{micro}m lysozyme crystals and 2-{micro}m crystals of Pdx1, an Arabidopsis enzyme involved in vitamin B6 biosynthesis. A seeding strategy was used to overcome the improbability of Pdx1 nucleation occurring with diminishing droplet volumes. Convection within droplets was exploited for rapid crystal mixing with ligands. Mixing times of <2 milliseconds were achieved. Droplet microfluidics for crystal size engineering and rapid micromixing can be used to advance time-resolved serial crystallography.

biochemistry↗

bioRxiv2024

Spatial interactions modulate tumor growth and immune infiltration

Direct observation of immune cell trafficking patterns and tumor-immune interactions is unlikely in human tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen formation evolve as a mechanism of immune escape, but the exact nature of the interaction between immune cells and collagen is poorly understood. Spatial data quantifying the degree of collagen fiber alignment in squamous cell carcinomas indicates that late stage disease is associated with highly aligned fibers. Here, we introduce a computational modeling framework (called Lenia) to discriminate between two hypotheses: immune cell migration that moves 1) parallel or 2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-ECM interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. We also illustrate the capabilities of Lenia to model the evolution of tumor progression and immune predation. Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining a kernel cell-cell interaction function that governs tumor growth dynamics under immune predation with immune cell migration. Mathematical modeling provides important mechanistic insights into cell interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interactions drives tumor growth and infiltration.

cancer biology↗

bioRxiv2023

Profiling plasma extracellular vesicle-derived microRNAs for noninvasive diagnosis of alpha-1 antitrypsin deficiency associated liver disease

Alpha-1 antitrypsin deficiency (AATD) is a rare genetic disease characterized by reduced circulating levels of alpha-1 antitrypsin (AAT) due to the retention of misfolded AAT in the hepatocytes. The toxic AAT aggregates in hepatocytes cause liver fibrosis, cirrhosis, and hepatocellular carcinoma. Most patients remain asymptomatic until the final stage in which liver transplantation becomes the only treatment. Timely diagnosis of AATD liver disease plays a critical role in intervention and finding curative solutions. Assessing the prevalence and severity of AATD liver disease remains challenging due to limitations in current methods. Liver biopsy, the gold standard for evaluating the hepatic AAT accumulation, the initiating stage for AATD liver disease, is hindered by invasiveness and sampling errors. To address these limitations, we conducted a study using unique and precious clinical samples. We analyzed plasma extracellular vesicle (EV) derived miRNAs and liver transcriptomes from AATD individuals to develop a sensitive and noninvasive diagnostic approach for AATD liver disease. In the testing stage of our study, we enrolled 17 AATD individuals with different stages of liver disease, as determined by liver biopsy, and 20 controls. We identified differential expression of 178 miRNAs within the AATD group compared to controls by miRNA sequencing. Among those miRNAs, we selected miR-223-3p, miR-23a-3p, miR-15b-5p, let-7a-5p, let-7f-5p, and miR-374a-5p for further validation in an independent cohort of 45 AATD individuals. Using a logistic model that combined three miRNAs, we achieved an AUC of 0.737 for detecting AATD liver disease. Adding a fourth miRNA into this model increased the AUC to 0.751. The changes in EV miRNAs are correlated to dysregulated expression of liver mRNAs in AATD individuals with different stages of liver disease. We propose that plasma-circulating EV exhibit distinct miRNAs in AATD individuals and could serve as clinically significant biomarkers for the early detection of AATD liver disease.

molecular biology↗

bioRxiv2023

Second-order effects of chemotherapy pharmacodynamics and pharmacokinetics on tumor regression and cachexia

Drug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we optimize treatment by comparing high variance schedules (e.g. high dose, low dose) with low variance schedules (constant dose). We extend a previous framework used to quantify second-order effects, known as antifragility theory, to investigate the role of drug pharmacokinetics. Using a simple one-compartment model, we find that high variance schedules are effective for a wide range of cumulative dose values. Next, using a mouse-parameterized two-compartment model of 5-fluorouracil, we show that the optimal schedule depends on initial tumor volume. Finally, we illustrate the trade-off between tumor response and lean mass preservation. Mathematical modeling indicates that high variance dose schedules provide a potential path forward in mitigating the risk of chemotherapy-associated cachexia by preserving lean mass without sacrificing tumor response.

cancer biology↗

bioRxiv2023

Ribosomal protein control of hematopoietic stem cell transformation through direct, non-canonical regulation of metabolism

We report here that expression of the ribosomal protein, RPL22, is frequently reduced in human myelodysplastic syndrome (MDS) and acute myelogenous leukemia (AML); and, reduced RPL22 expression is associated with worse outcomes. Mice null for Rpl22 display characteristics of an MDS-like syndrome and develop leukemia at an accelerated rate. Rpl22-deficient mice also display enhanced hematopoietic stem cell (HSC) self-renewal and obstructed differentiation potential, which arises not from reduced protein synthesis but from altered metabolism including increased fatty acid oxidation (FAO) and a striking induction of the stemness factor Lin28b in the resulting leukemia. Lin28b promotes a substantial increase in lipid content, upon which the survival of Rpl22-deficient leukemias depends. Altogether, these findings reveal that Rpl22 insufficiency enhances the leukemia potential of HSC through regulation of FAO and promotes leukemogenesis through Lin28b promotion of lipid synthesis. HighlightsO_LIRPL22 insufficiency is observed in MDS/AML and is associated with reduced survival C_LIO_LIRpl22-deficiency produces an MDS-like syndrome and facilitates leukemogenesis C_LIO_LIRpl22-deficiency does not impair global protein synthesis by HSC C_LIO_LIRpl22 controls leukemia survival through control of lipid synthesis C_LI eTOC: Rpl22 controls the function and transformation potential of hematopoietic stem cells through regulation of lipid metabolism.

cancer biology↗

bioRxiv2023

Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling

Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumors treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from in vitro Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments in vivo confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

cancer biology↗

bioRxiv2023

Games and the treatment convexity of cancer

Evolutionary game theory has been highly valuable in studying frequency-dependent selection and growth between competing cancer phenotypes. We study the connection between the type of competition as defined by properties of the game, and the convexity of the treatment response function. Convexity is predictive of differences in the tumors response to treatments with identical cumulative doses delivered with different variances. We rely on a classification of 2 x 2 games based on the signs of dilemma strengths, containing information about the kind of selection through the games equilibrium structure. With the disease starting in one game class, we map the type of effects treatment may have on the game depending on dosage and the implications of treatment convexity. Treatment response is a linear function of dose if the game is a prisoners dilemma, coordination, or harmony game and does not change game class, but may be convex or concave for anti-coordination games. If the game changes class, there is a rich variety in response types including convex-concave and concave-convex responses for transitions involving anti-coordination games, response discontinuity in case of a transition out of coordination games, and hysteresis in case of a transition through coordination games.

cancer biology↗

bioRxiv2023

Mechanisms of SARS-CoV-2 Inactivation using UVC Laser Radiation

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has had a tremendous impact on humanity. Prevention of transmission by disinfection of surfaces and aerosols through a chemical-free method is highly desirable. Ultraviolet C (UVC) light is uniquely positioned to achieve inactivation of pathogens. We report the inactivation of SARS-CoV-2 virus by UVC radiation and explore its mechanisms. A dose of 50mJ/cm2 using a UVC laser at 266nm achieved an inactivation efficiency of 99.89%, whilst infectious virions were undetectable at 75mJ/cm2 indicating >99.99% inactivation. Infection by SARS-CoV-2 involves viral entry mediated by the spike glycoprotein (S), and viral reproduction, reliant on translation of its genome. We demonstrate that UVC radiation damages ribonucleic acid (RNA) and provide in-depth characterisation of UVC-induced damage of the S protein. We find that UVC severely impacts SARS-CoV-2 spike proteins ability to bind human angiotensin-converting enzyme 2 (hACE2) and this correlates with loss of native protein conformation and aromatic amino acid integrity. This report has important implications for the design and development of rapid and effective disinfection systems against the SARS-CoV-2 virus and other pathogens.

microbiology↗

bioRxiv2022

The sum and the parts: dynamics of multiple and individual metastases during adaptive therapy

Evolutionary therapies, such as adaptive therapy, have shown promise in delaying treatment resistance in late-stage cancers. By alternating between drug applications and drug-free vacations, competition between sensitive and resistant cells can be exploited to maximize the time to progression. However, the optimal schedule of this dosing regimen depends on the properties of the tumor, which often are not directly measurable in clinical practice. In this work, we propose that the initial cycle of adaptive therapy can be used as a tool to probe the relevant tumor properties. We present a framework for estimating individual and collective components of a metastatic system through tumor response dynamics, which uses a system of off-lattice agent-based models to represent individual metastatic lesions within independent domains, all of which are subject to the same systemic therapy. We find that the first cycle of adaptive therapy delineates several features of the computational metastatic system: larger metastases have longer cycles; a higher proportion of drug resistant cells slows the cycles; and a faster cell turnover rate speeds up drug response time and slows the regrowth time. The number of metastases does not affect cycle times, as response dynamics are dominated by the largest tumors rather than the aggregate. In addition, the heterogeneity of the system is also a guide for therapeutic approaches: generally, systems with more between-tumor (intertumor) heterogeneity had better success with continuous therapy, while systems with more within-tumor (intratumor) heterogeneity responded better to adaptive therapy. Intertumor heterogeneity was found to correlate more with dynamics from patients with high and low Gleason scores while intratumor heterogeneity was correlated with dynamics from patients with intermediate Gleason scores.

systems biology↗

bioRxiv2022

Cigarette smoke exposed airway epithelial cell-derived EVs promote pro-inflammatory macrophage activation in alpha-1 antitrypsin deficiency

BackgroundAlpha-1 antitrypsin deficiency (AATD) is a genetic disorder most commonly secondary to a single mutation in the SERPINA1 gene (PI*Z) that causes misfolding and accumulation of alpha-1 antitrypsin (AAT) in hepatocytes and mononuclear phagocytes which reduces plasma AAT and creates a toxic gain of function. This toxic gain of function promotes a pro-inflammatory phenotype in macrophages that contributes to lung inflammation and early-onset COPD, especially in individuals who smoke cigarettes. The aim of this study is to determine the role of cigarette exposed AATD macrophages and bronchial epithelial cells in AATD-mediated lung inflammation. MethodsPeripheral blood mononuclear cells from AATD and healthy individuals were differentiated into alveolar-like macrophages and exposed to air or cigarette smoke while in culture. Macrophage endoplasmic reticulum stress was quantified and secreted cytokines were measured using qPCR and cytokine ELISAs. To determine whether there is "cross talk" between epithelial cells and macrophages, macrophages were exposed to extracellular vesicles released by airway epithelial cells exposed to cigarette smoke and their inflammatory response was determined. ResultsAATD macrophages spontaneously produce several-fold more pro-inflammatory cytokines as compared to normal macrophages. AATD macrophages have an enhanced inflammatory response when exposed to cigarette smoke-induced extracellular vesicles (EVs) released from airway epithelial cells. Cigarette smoke-induced EVs induce expression of GM-CSF and IL-8 in AATD macrophages but have no effect on normal macrophages. Release of AAT polymers, potent neutrophil chemo attractants, were also increased from AATD macrophages after exposure to cigarette smoke-induced EVs. ConclusionsThe expression of mutated AAT confers an inflammatory phenotype in AATD macrophages which disposes them to an exaggerated inflammatory response to cigarette smoke-induced EVs, and thus could contribute to progressive lung inflammation and damage in AATD individuals.

cell biology↗

bioRxiv2022

Tumor-immune metaphenotypes orchestrate an evolutionary bottleneck that promotes metabolic transformation

Metabolism plays a complex role in the evolution of cancerous tumors, including inducing a multifaceted effect on the immune system to aid immune escape. Immune escape is, by definition, a collective phenomenon by requiring the presence of two cell types interacting in close proximity: tumor and immune. The microenvironmental context of these interactions is influenced by the dynamic process of blood vessel growth and remodelling, creating heterogeneous patches of well-vascularized tumor or acidic niches. We present a multiscale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shapes acid-mediated invasion and immune escape over a biologically-realistic time scale. We model immune escape mechanisms such as i) acid inactivation of immune cells, ii) competition for glucose, and iii) inhibitory immune checkpoint receptor expression (PD-L1) under anti-PD-L1 and sodium bicarbonate buffer therapies. To aid in understanding immune escape as a collective cellular phenomenon, we define immune escape in the context of six collective phenotypes (termed "meta-phenotypes"): Self-Acidify, Mooch Acid, PD-L1 Attack, Mooch PD-L1, Proliferate Fast, and Starve Glucose. Fomenting a stronger immune response leads to initial benefits but this advantage is offset by increased cell turnover that accelerates the emergence of aggressive phenotypes by inducing an evolutionary bottleneck. This model helps to untangle the key constraints on evolutionary costs and benefits of three key phenotypic axes on tumor invasion and treatment: acid-resistance, glycolysis, and PD-L1 expression. The benefits of concomitant anti-PD-L1 and buffer treatments is a promising treatment strategy to limit the adverse effects of immune escape. O_TEXTBOXSignificance statement In this work, we present a multi-scale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shapes acid-mediated invasion and immune escape over a biologically-realistic time scale. To aid in understanding immune escape as a collective cellular phenomenon, we introduce the concept of metaphenotypes: immune escape mechanisms that account for cellular phenotype and surrounding context. Metaphenotypes are defined by accounting for the following: cellular phenotype, microenvironmental factors, neighboring cell types, and immune-tumor interactions. These metaphenotypes provide insight into why targeting intratumoral pH with bicarbonate buffer is a synergistic combination treatment when paired with immune checkpoint blockade. C_TEXTBOX

cancer biology↗

bioRxiv2022

DNA co-methylation has a stable structure and is related to specific aspects of genome regulation

DNA methylation (DNAm) is influenced by genetic and environmental factors, and can be used to understand interindividual variability in genomic regulation. Co-methylation between DNAm sites is a known phenomenon, but the architecture of relationships between the approximately 450,000 (450k) sites commonly measured in epidemiological studies has not been described. We investigate whether interindividual co-methylation structure amongst the 450k sites changes with age, whether it differs between UK-born White (n=849, 910, 921 and 424) and Pakistani ancestry (n=439) individuals, and how it relates to genome regulation. We find stability between birth and adolescence, across cohorts, and between two ethnic groups. Highly correlated DNAm sites in close proximity are heritable, but these relationships are weakly influenced by nearby genetic variants, and are enriched for transcription factor (TF) binding sites related to regulation of short RNAs transcribed by RNA polymerase III. Highly correlated sites that are distant, or on different chromosomes (in trans), are driven by common and unique environmental factors, with methylation at these sites less likely to be driven by genotype. Trans co-methylated DNAm sites are enriched for multiple TF binding sites and for inter-chromosomal chromatin contact sites, suggesting DNA co-methylation of distant sites may relate to long-range cooperative TF interactions. We conclude that DNA co-methylation has a stable structure from birth to adolescence, and between UK-born White and Pakistani individuals. This stable structure might have implications for future design and interpretation of epigenetic studies. We hypothesise that co-methylation may have roles in genome regulation in humans, including 3D chromatin architecture.

bioinformatics↗

bioRxiv2021

Marker-free characterization of single live circulating tumor cell full-length transcriptomes

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor that hinders enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypic traits relative to the tumor of origin. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from the peripheral blood. Most CTC detection methods use a mix of size selection, immune marker based white blood cells (WBC) depletion, and positive enrichment antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. Single-cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate and is a potent method of enabling unbiased identification of CTCs. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analysis tasks. These include a novel method of scRNA-Seq clustering, named Deep Dictionary Learning using K-means clustering cost (DDLK), expression based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-Seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell(R) FX and PolarisTM workflow that works by the principles of size-based separation of CTCs and marker based WBC depletion.

bioinformatics↗

bioRxiv2021

Gattaca: Base pair resolution mutation tracking for somatic evolution studies using agent-based models

Gattaca provides the first base-pair resolution artificial genomes for tracking somatic mutations within agent based modeling. Through the incorporation of human reference genomes, mutational context, sequence coverage/error information Gattaca is able to realistically provide comparable sequence data for in-silico comparative evolution studies with human somatic evolution studies. This user-friendly method, incorporated into each in-silico cell, allows us to fully capture somatic mutation spectra and evolution.

evolutionary biology↗

bioRxiv2021

Mistic: an open-source multiplexed image t-SNE viewer

Understanding the complex ecology of a tumor tissue and the spatio-temporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immune-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. In this work, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be taken from t-SNE or UMAP coordinates. This grouped view of all the images further aids an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype or to select images for subsequent downstream analysis. Currently there is no freely available tool to generate such image t-SNEs. Mistic is open-source and can be downloaded at: https://github.com/MathOnco/Mistic.

cancer biology↗