Biology subjects

Iyer, A.

Publications and source records attributed to Iyer, A..

51 records · Page 3Linked to original sources

bioRxiv2020

Oxygen-independent chemogenetic protein tags for live-cell fluorescence microscopy

Fluorescent proteins enable targeted visualization of biomolecules in living cells, but their maturation is oxygen-dependent and they are susceptible to aggregation and/or suffer from poor photophysical properties. Organic fluorophores are oxygen-independent with superior photophysical properties, but targeting biomolecules in vivo is challenging. Here, we introduce two oxygen-independent chemogenetic protein (OICP) tags that impart fluorogenicity and fluorescence lifetime enhancement to bound organic dyes. We present a photo- and physicochemical characterization of thirty fluorophores interacting with two OICPs and conclude that aromatic planar structures bind with high specificity to the hydrophobic pockets of the proteins. The binding specificity of the tags and the superior photophysical properties of organic fluorophores enable microscopy of living bacterial and eukaryotic cells. The exchange of photobleached dye for unbleached fluorophore enables prolonged live-cell imaging. Our protein tags provide a general tool for investigating (sub)cellular protein localization and dynamics, protein-protein interactions, and microscopy applications under strictly oxygen-free conditions.

biochemistry↗

bioRxiv2020

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier

Single-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

bioinformatics↗

bioRxiv2020

The neoantigen landscape of mycosis fungoides

BackgroundMycosis fungoides (MF), the most common type of cutaneous T-cell lymphoma, has a dismal prognosis in advanced stages. Treatments for advanced disease are mostly palliative and MF remains incurable. Although MF is a known immunogenic neoplasm, immunotherapies such as interferons and the immune checkpoint inhibitors yield inconsistent results. Since the number, HLA-binding strength and subclonality of neoantigens are correlated with the therapeutic responses, we aimed here to characterize the landscape of neoantigens in MF. MethodsWe conducted whole exome and whole transcriptome sequencing of 24 MF samples (16 plaque, 8 tumour) from 13 patients. Bioinformatic pipelines (Mutect2, OptiType, MuPeXi) were used for in silico mutation calling, HLA typing, and neoantigen prediction. Phylogenetic analysis was used to subdivide the malignant cell population into stem and clades (subclones). Clonality of neaontigens was determined by matching neoantigens to the stem and clades of the phylogenetic tree of each MF sample. ResultsMF has a high mutational load (median 3217 non synonymous mutations), resulting in a significant number of total neoantigens (median 1309 per sample) and high-affinity neoantigens (median 328). In stage I disease most neoantigens were clonal but with progression to stage II, subclonal neoantigens comprised >50% of the total. There was very little overlap in neoantigens across patients or between different lesions on the same patient, indicating a high degree of genetic heterogeneity. ConclusionsAnalysis of the neoantigen landscape of MF revealed a very high neoantigen load and thus a significant immunogenic potential of this lymphoma. However, neoantigenic heterogeneity and significant subclonality might limit the efficacy of immunotherapy. We hypothesize that neoantigen number and subclonality might be useful biomarkers determining sensitivity to immunotherapeutic strategies.

cancer biology↗

bioRxiv2019

Integrative analysis and machine learning based characterization of single circulating tumor cells

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic systems for label-free enrichment of CTCs.

genomics↗

bioRxiv2019

Optimal Mass Transport for Robust Texture Analysis

The emerging field of radiomics, which consists of transforming standard-of-care images to quantifiable scalar statistics, endeavors to reveal the information hidden in these macroscopic images. This field of research has found different applications ranging from phenotyping and tumor classification to outcome prediction and treatment planning. Texture analysis, which often consists of reducing spatial texture matrices to summary scalar features, has been shown to be important in many of the latter applications. However, as pointed out in many studies, some of the derived texture statistics are strongly correlated and tend to contribute redundant information; and are also sensitive to the parameters used in their computation, e.g., the number of gray intensity levels. In the present study, we propose first to consider texture matrices, with an emphasis on gray-level co-occurrence matrix (GLCM), as a non-parametric multivariate objects. The proposed modeling approach avoids evaluating redundant and strongly correlated features and also prevents the feature processing steps. Then, via the Wasserstein distance from optimal mass transport theory, we propose to compare these spatial objects to identify computerized tomography slices with dental artifacts in head and neck cancer. We demonstrate the robustness of the proposed classification approach with respect to the GLCM extraction parameters and the size of the training set. Comparisons with the random forest classifier, which is constructed on scalar texture features, demonstrates the efficiency and robustness of the proposed algorithm.

bioengineering↗

bioRxiv2019

Independent evolution of cutaneous lymphoma subclones in different microenvironments of the skin

Mycosis fungoides (MF) is the most common, yet incurable, cutaneous T-cell lymphoma. We have recently shown that the disease is initiated by hematogenous seeding the skin with clonotypically diverse neoplastic T-cells which proliferate accumulating numerous mutations and produce lesions of high intratumoral heterogeneity (ITH). A characteristic but a poorly studied feature of MF is epidermotropism, the tendency to infiltrate skin epithelial layer (epidermis) in addition to the vascularized dermis. By sequencing the exomes of the microdissected clusters of lymphoma cells from the epidermis and the dermis, we found that those microenvironments harbored different malignant clonotypes and exhibited different patterns of driver gene mutation. Phylogenetic relationships between cancer subclones witnessed to the independent mutational evolution in the epidermis and dermis. Thus, the invasion of MF to different skin layers does not occur by gradual infiltration of the expanding tumor mass, but is caused by separate seeding processes with different malignant clones that develop independently of one another via a neutral, branched evolution. In conclusion, tissue microenvironments shape the subclonal architecture in MF leading to \"ecological heterogeneity\" which contributes to the total ITH. Since ITH adversely affects cancer prognosis, targeting the microenvironment may present therapeutic opportunities in MF and other cancers.

cancer biology↗

bioRxiv2019

Branched evolution and genomic intratumor heterogeneity in the pathogenesis of cutaneous T-cell lymphoma

Mycosis fungoides (MF) is a slowly progressive cutaneous T-cell lymphoma (CTCL) for which there is no cure. In the early plaque stage the disease is indolent, but development of tumors heralds an increased risk of metastasis and death. Previous research into the genomic landscape of CTCL revealed a complex pattern of >50 driver mutations implicated in more than a dozen of signaling pathways. However, the genomic mechanisms governing disease progression and treatment resistance remain unknown. Building on our previous discovery of the clonotypic heterogeneity of MF, we hypothesized that this lymphoma does not progress in a linear fashion as currently thought, but comprises heterogeneous mutational subclones. We sequenced exomes of 49 cases of MF and identified 28 previously unreported putative driver genes. MF exhibited extensive intratumoral heterogeneity (ITH) of a median of six subclones showing branched pattern of phylogenetic relationships. Stage progression was correlated with an increase in ITH and redistribution of mutations from the stem to the clades. The pattern of clonal driver mutations was highly variable with no consistent mutations between patients. A similar intratumoral heterogeneity was detected in leukemic CTCL (Sezary syndrome). Based on these findings we propose a model of the pathogenesis of MF comprising neutral, divergent evolution of cancer subclones and discuss how ITH impacts the efficacy of targeted drug therapies and immunotherapies of CTCL.

cancer biology↗

bioRxiv2019

Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

PurposeTo construct robust and validated radiomic predictive models, the development of a reliable method that can identify reproducible radiomic features robust to varying image acquisition methods and other scanner parameters should be preceded with rigorous validation. Due to the property of high correlation present between radiomic features, we hypothesize that reproducible radiomic features across different datasets that are obtained from different image acquisition settings preserve some level of connectivity between features in the form of a network.\n\nMethodsWe propose a regularized partial correlation network to identify robust and reproducible radiomic features. This approach was tested on two radiomic feature sets generated with two different reconstruction methods from a cohort of 47 lung cancer patients. The commonality of the resulting two networks was assessed. A largest common network component from the two networks was tested on phantom data consisting of 5 cancer samples. We further propose a novel K-means algorithm coupled with the optimal mass transport (OMT) theory to cluster samples. This approach following the regularized partial correlation analysis was tested on computed tomography (CT) scans from 77 head and neck cancer patients that were downloaded from The Cancer Imaging Archive (TCIA) and validated on CT scans from 83 head and neck cancer patients treated at our institution.\n\nResultsCommon radiomic features were found in relatively large network components between the resulting two partial correlation networks from a cohort of 47 lung cancer patients. The similarity of network components in terms of the common number of radiomic features was statistically significant. For phantom data, the Wasserstein distance on a largest common network component from the lung cancer data was much smaller than the Wasserstein distance on the same network using random radiomic features, implying the reliability of those radiomic features present in the network. Further analysis using the proposed Wasserstein K-means algorithm on TCIA head and neck cancer data showed that the resulting clusters separate tumor subsites and this was validated on our institution data.\n\nConclusionsWe showed that a network-based analysis enables identifying reproducible radiomic features. This was validated using phantom data and external data via the Wasserstein distance metric and the proposed Wasserstein K-means method.

biophysics↗

bioRxiv2019

Library of model implementations for sharing deep-learning image segmentation and outcomes models

An open-source library of implementations for deep-learning based image segmentation and outcomes models is presented in this work. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. The library was developed with Computational Environment for Radiological Research (CERR) software platform. CERR is a natural choice to centralize model implementations due to its comprehensiveness, popularity, and ease of use. CERR provides well-validated feature extraction for radiotherapy dosimetry and radiomics with fine control over the calculation settings. This allows users to select the appropriate feature calculation used in the model derivation. Models for automatic image segmentation are distributed via Singularity containers, with seamless i/o to and from CERR. Singularity containers allow for segmentation models to be deployed with a variety of scientific computing architectures. Deployment of models is driven by JSON configuration file, making it convenient to plug-in models. Models from the library can be called programmatically for batch evaluation. The library includes implementations for popular radiotherapy models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from Image Biomarker Standardization features found to be important across multiple sites and image modalities. Deep learning-based image segmentation models include state of the art networks such as Deeplab and other problem-specific architectures. The library is distributed as GNU-copyrighted software at https://www.github.com/cerr/CERR.

bioinformatics↗

bioRxiv2019

Deep learning-based auto-segmentation of swallowing and chewing structures in CT

PurposeDelineating the swallowing and chewing structures in Head and Neck (H&N) CT scans is necessary for radiotherapy treatment (RT) planning to reduce the incidence of radiation-induced dysphagia, trismus, and speech dysfunction. Automating this process would decrease the manual input required and yield reproducible segmentations, but generating accurate segmentations is challenging due to the complex morphology of swallowing and chewing structures and limited soft tissue contrast in CT images. MethodsWe trained deep learning models using 194 H&N CT scans from our institution to segment the masseters (left and right), medial pterygoids (left and right), larynx, and pharyngeal constrictor muscle using DeepLabV3+ with the resnet-101 backbone. Models were trained in a sequential manner to guide the localization of each structure group based on prior segmentations. Additionally, an ensemble of models was developed using contextual information from three different views (axial, coronal, and sagittal), for robustness to occasional failures of the individual models. Output probability maps were averaged, and voxels were assigned labels corresponding to the class with the highest combined probability. ResultsThe median dice similarity coefficients (DSC) computed on a hold-out set of 24 CT scans were 0.87{+/-}0.02 for the masseters, 0.80{+/-}0.03 for the medial pterygoids, 0.81{+/-}0.04 for the larynx, and 0.69{+/-}0.07for the constrictor muscle. The corresponding 95th percentile Hausdorff distances were 0.32{+/-}0.08cm (masseters), 0.42{+/-}0.2cm (medial pterygoids), 0.53{+/-}0.3cm (larynx), and 0.36{+/-}0.15cm (constrictor muscle). Dose-volume histogram (DVH) metrics previously found to correlate with each toxicity were extracted from manual and auto-generated contours and compared between the two sets of contours to assess clinical utility. Differences in DVH metrics were not found to be statistically significant (p>0.05) for any of the structures. Further, inter-observer variability in contouring was studied in 10 CT scans. Automated segmentations were found to agree better with each of the observers as compared to inter-observer agreement, measured in terms of DSC. ConclusionsWe developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT. The resulting segmentations can be included in treatment planning to limit complications following RT for H&N cancer. The segmentation models developed in this work are distributed for research use through the open-source platform CERR, accessible at https://github.com/cerr/CERR.

bioinformatics↗

bioRxiv2019

Skin colonization by circulating neoplastic clones in cutaneous T-cell lymphoma

Mycosis fungoides (MF) is a mature T-cell lymphoma currently thought to develop primarily in the skin by a clonal expansion of a transformed, resident memory T-cell. However, this concept does not explain the key characteristics of MF such as the debut with multiple, widespread skin lesions or inability of skin directed therapies to provide cure. The testable inference of the mature T-cell theory is the clonality of MF with respect to all rearranged T-cell receptor (TCR) genes. Here we have used whole exome sequencing approach to detect and quantify TCR, -{beta} and -{gamma} clonotypes in tumor cell clusters microdissected from MF lesions. This method allows us to calculate the tumor cell fraction of the sample and therefore an unequivocal identification of the TCR clonotypes as neoplastic. Analysis of TCR sequences from 29 patients with MF stage I-IV proved existence of multiple T-cell clones within the tumor cell fraction, with a considerable variation between patients and between lesions from the same patient (median 11 clones, range 2-80 clones/sample). We have also detected multiple neoplastic clones in the peripheral blood in all examined patients. Based on these findings we propose that circulating neoplastic T-cell clones continuously replenish the lesions of MF thus increasing their heterogeneity by a mechanism analogous to the consecutive tumor seeding. We hypothesize that circulating neoplastic clones might be a promising target for therapy and could be exploited as a potential biomarker in MF.

cancer biology↗

bioRxiv2018

Precursor miRNAs are trafficked along axons associated with vesicles and locally processed to regulate growth cone steering

Various species of non-coding RNAs (ncRNAs) are enriched in subcellular compartments but the mechanisms orchestrating their delocalization and their local functions remain largely unknown. We investigated both aspects using the elongating retinal ganglion cell axon and its tip, the growth cone, as models. We reveal that specific endogenous precursor microRNAs (pre-miRNAs) are actively trafficked, anchored to CD63-positive vesicles, to distal axons along microtubules. Upon exposure to the chemotropic cue Sema3A, pre-miRNAs are processed specifically within axons into newly synthesized mature miRNAs, which, in turn, silence the basal translation of TUBB3 but not of APP. At the organismal level, these mature miRNAs are required for growth cone steering and a fully functional visual system. Overall, our results uncover a novel mode of ncRNA transport from one cytosolic compartment to another within polarized cells. They also reveal that newly synthesized miRNAs are critical components of a ncRNA-based signaling pathway that transduces environmental signals into the structural remodelling of subcellular compartments.\n\nHighlightsO_LIPrecursor miRNAs are actively transported along axons to the growth cone tethered to CD63-positive vesicles\nC_LIO_LISema3A but not Slit2 induces the local biogenesis of specific miRNAs within axons\nC_LIO_LIMature miRNAs are important for growth cone responsiveness ex vivo and the establishment of functional connections in vivo\nC_LIO_LINewly synthesized miRNAs inhibit the basal translation of TUBB3 but not APP upon Sema3A exposure\nC_LI

cell biology↗

bioRxiv2018

Clonotypic Heterogeneity In Cutaneous T-Cell Lymphoma Revealed By Comprehensive Whole Exome/Transcriptome Sequencing

Mycosis fungoides (MF), the most common type of cutaneous T-cell lymphoma, is believed to represent a clonal expansion of a transformed skin resident memory T-cell. T-cell receptor (TCR) clonality (i.e. identical sequences of rearranged TCR, {beta} and {gamma}), the key premise of this hypothesis, has been difficult to document conclusively because malignant cells are not readily distinguishable from the tumor infiltrating, reactive lymphocytes, which contribute to the TCR clonotypic repertoire of MF. Here we have successfully adopted the technique of targeted whole exome and whole transcriptome sequencing (WES/WTS) to identify the repertoire of rearranged TCR genes in tumor enriched samples from patients with MF. Although most of the investigated biopsies of MF had the expected monoclonal rearrangements of TCR{gamma} of the frequency corresponding to the frequency of tumor cells, in half of the samples we detected multiple (up to seven) TCR and -{beta} clonotypes by WES and WTS. Our findings are compatible with the model in which the initial malignant transformation in MF does not occur in mature, memory T-cells but rather at the level of T-lymphocyte progenitor after TCR{gamma} rearrangement but before TCR{beta} or TCR rearrangements. The WES/WTS method is potentially applicable to other types of T-cell lymphomas and enables comprehensive characterization of the TCR repertoire and mutational landscape in these malignancies.

cancer biology↗

bioRxiv2018

Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis

Signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including \"novel substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.

systems biology↗

bioRxiv2018

Pisces: An Accurate and Versatile Variant Caller for Somatic and Germline Next-Generation Sequencing Data

MotivationNext-Generation Sequencing (NGS) technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants, suitable for the clinic.\n\nResultsWe have developed Pisces, a rapid, versatile and accurate small variant calling suite designed for somatic and germline amplicon sequencing applications. Pisces accuracy is achieved by four distinct modules, the Pisces Read Stitcher, Pisces Variant Caller, the Pisces Variant Quality Recalibrator, and the Pisces Variant Phaser. Each module incorporates a number of novel algorithmic strategies aimed at reducing noise or increasing the likelihood of detecting a true variant.\n\nAvailabilityPisces is distributed under an open source license and can be downloaded from https://github.com/Illumina/Pisces. Pisces is available on the BaseSpace SequenceHub as part of the TruSeq Amplicon workflow and the Illumina Ampliseq Workflow. Pisces is distributed on Illumina sequencing platforms such as the MiSeq, and is included in the Praxis Extended RAS Panel test which was recently approved by the FDA for the detection of multiple RAS gene mutations.\n\nContactpisces@illumina.com\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗